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Journal of Evaluation in Clinical Practice ISSN 1365-2753
Selecting clinical diagnoses: logical strategies informed
by experience
Donald Edward Stanley FCAP1 and Daniel G. Campos PhD2
1
Pathologist, Department of Pathology, Maine Medical Center, Portland, Maine, USA
Associate Professor, Department of Philosophy, Brooklyn College, The City University of New York, New York, NY, USA
2
Keywords
abduction, Bayesian probability, diagnosis,
hypothesis, induction, Peirce
Correspondence
Dr Donald Edward Stanley
Associates in Pathology
500 West Neck Road
Nobleboro, Maine 04555
USA
E-mail: dstanley@tidewater.net
Accepted for publication: 8 June 2015
Abstract
This article describes reasoning strategies used by clinicians in different diagnostic circumstances and how these modes of inquiry may allow further insight into the evaluation
and treatment of patients. Specifically, it aims to make explicit the implicit logical considerations that guide a variety of strategies in the diagnostic process, as exemplified in
specific clinical cases. It focuses, in particular, in strategies that clinicians use to move from
a large set of possible diagnoses initially suggested by abductive inferences – the process
of hypothesis generation that creates a diagnostic space – to a narrower set or even to a
single ‘best’ diagnosis, where the criteria to determine what is ‘best’ may differ according
to different strategies. Experienced clinicians should have a diversified kit of strategies – for
example, Bayesian probability or inference to a lovely explanation – to select from among
previously generated hypotheses, rather than rely on any one approach every time.
doi:10.1111/jep.12417
Introduction
In this article, we shall describe how reasoning strategies identified
by the American pragmatists and other philosophers are used by
clinicians under different diagnostic circumstances and how these
modes of inquiry may allow further insight into the evaluation and
treatment of patients.
Specifically, we aim to describe and make explicit the implicit
logical considerations that guide a variety of strategies in the
diagnostic process, as exemplified in specific clinical cases.
We are interested, in particular, in strategies that clinicians use
to move from a large set of possible diagnoses initially suggested
by abductive inferences – the diagnostic space – to a narrower
set of testable diagnoses or even to a single ‘best’ diagnosis,
where the criteria to determine what is ‘best’ may differ according to different strategies. Experienced clinicians should have a
diversified kit of strategies rather than relying on any one
approach every time.
Our starting point is our previous thesis that the diagnostic
process begins with an abductive search for the differential diagnosis. Clinicians begin by knowing nothing definitive about the
origin of the signs and symptoms and search for their explanation
through abductive conjecturing [1]. By abduction, we mean the
logical process of forming an explanatory hypothesis and, following Charles Peirce, we hold that even though abduction only
Journal of Evaluation in Clinical Practice (2015) © 2015 John Wiley & Sons, Ltd.
asserts its conclusion ‘problematically or conjecturally’, it is an
inference that has a definite logical form, namely
The surprising fact, C, is observed;
But if A were true, C would be a matter of course.
Hence, there is reason to suspect that A is true [2].
For example, a doctor may auscultate a dyspneic patient’s chest
for crackles. She reasons that if the patient had pulmonary congestion, then crackles would be a predictable consequence. Therefore, she conjectures that the patient may have pulmonary
congestion.
For Peirce, ‘A cannot be abductively inferred, or . . . cannot be
abductively conjectured, until its entire contents is already present
in the premiss, “If A were true, C would be a matter of course”’ [2].
This premise amounts to stating that A would explain C, so Peirce
is arguing that we cannot infer A (pulmonary congestion) unless
our inference is to an explanatory hypothesis. In short, a condition
for the admissibility of a hypothesis is that the hypothesis would
account for the facts (crackles), and on those explanatory grounds
we hold the hypothesis to be plausible.
However, for any given set of symptoms, doctors often may
conjecture many possible diagnoses; thus, they also attempt to
limit the practical error of trying to know everything, and thereby
generating too many hypotheses for testing. We have proposed that
the elegance of practical diagnosis lies in striking a balance
between the generation of hypotheses and the selection of the most
1
Selecting clinical diagnoses
appropriate – the highest practical ranking – for testing [1]. In this
article, we are focused on the latter, selective stage of diagnostic
reasoning.1
Through detailed cases, we will describe several experimental
strategies used by clinicians to select plausible diagnoses for
further testing or treatment, including
1 Selection of hypotheses by deducing consequences from them
and testing experimentally for whether the consequences are realized (Peircean logical inquiry);
2 Assessment of the probability of hypotheses by way of Bayesian
and/or frequentist statistical approaches;
3 Selection of hypotheses by way of inference to the loveliest
explanation (a version of inference to the best explanation);
4 Selection of hypotheses by criteria such as uberty or fecundity
with potential truth, simplicity, explanatory power, theoretical unification and so on, and
5 Combinations of these – for example, estimating the likelihood
of diagnostic hypotheses given the evidence through surrogate
criteria such as uberty or loveliness, or estimating prior Bayesian
probabilities through clinical acumen resulting from previous
experiences.
According to the Peircean account, after generating a plausible
explanatory hypothesis, the scientific inquirer deduces the observable consequences that should follow from that hypothesis. Then,
he or she tests experimentally whether those consequences are in
fact realized. This is the abductive-deductive-inductive pattern of
logical inquiry [2]. Besides being able to deduce testable consequences, however, there are further considerations as to which
hypotheses are worthy of testing. At the outset of inquiry, we may
abductively generate and tentatively uphold for further scrutiny
fertile hypotheses, even if they may seem unlikely or a priori
improbable. Peirce distinguishes between ‘security’ and ‘uberty’ in
inferential reasoning. The security of an inference is its degree of
certainty; the uberty of an inference – especially the inference to a
plausible hypothesis – is its potential to lead to undiscovered truth.
The uberty of an abductive hypothesis is different from its a priori
likeliness, and even from its immediate fruitfulness in solving a
problem. However, there tends to be a tension between security
and uberty intrinsic to scientific reasoning. Secure reasonings,
such as deductions and well-tested inductions, do not open new
paths of investigation, new hypotheses, that may be ‘gravid’ with
potential truth, even if these paths seem risky and a priori unlikely.
The upshot is that in conflating the scientific generation of gravid
hypotheses with an evaluation of their likeliness, we neglect the
1
Ross Upshur has argued previously that the ‘dispute between the proponents of clinical common sense and evidence-based medicine can likely
find common ground in the philosophy of C.S. Peirce’ [13]. He advised that
‘circumspection must accompany the use of statistical models in clinical
reasoning’ and claimed that Peirce’s logic provides ‘a promising framework in which to develop a theory of clinical reasoning that is both
rigorous and probabilistic [while also being] able to recognize the uncertainties and particularities of day-to-day clinical practice’ [13]. He did not
develop, however, a detailed account of this theory of clinical reasoning.
Here we aim to develop one important aspect of it, by expounding the
theory and illustrating the practice of diagnosis selection. In this diagnostic
process, clinicians must have recourse to a variety of logical strategies in
which clinical acumen, insight and experience play a central role to guide
statistical, economic, and other considerations.
2
D.E. Stanley and D.G. Campos
need for generating, pondering and pursuing hypotheses that are
rich in possibilities, even if they are a priori improbable.
On the other hand, the Peircean emphasis on uberty seems most
appropriate for theoretical scientific inquiry. However, we have
suggested that the situation in medical diagnosis is different, due to
the immediacy of its practical aims: we must find, as economically
and quickly as possible, the explanation for the patient’s condition.
Therefore, probabilistic considerations may enter more quickly
into the assessment and selection of hypotheses for testing. In
other words, once explanatory hypotheses are generated, considerations as to their likeliness to be true soon become important [1].
Questions arise, then, regarding how to assess the likeliness of
the candidate explanatory hypotheses. Doctors may, for instance,
adopt a Bayesian approach and try to ascertain the probability of
the diagnosis (hypothesis) given the evidence – symptoms, preliminary test results and so on.
We thus will pay particular attention to the Bayesian model of
hypothesis selection and its possible variants or modifications. By
Bayesian selection, we mean the process through which, once
diagnoses have been generated abductively, the clinician ranks
them probabilistically or quasi-probabilistically in order to select
the best for testing or treatment.
Recall the formulation of Bayes’ rule to estimate conditional
probabilities:
P ( H E ) = [ P ( E H ) × P ( H )] P ( E ) .
This is often interpreted as meaning that the conditional probability that a hypothesis H is true given the evidence E can be estimated from: (a) the probability of the evidence given the
supposition that the hypothesis is true – P(E/H), that is, the probability that E will occur if H is true – ; (b) the probability of the
evidence – P(E), that is, the statistical frequency with which the
facts counted as evidence happen in general – , and the probability
that the hypothesis is true, P(H).2
A drawback of with this strategy is the difficulty of establishing
the prior probability of the hypothesis, P(H). This is often considered to be a purely subjective guess on the part of an expert.
Presumably, however, the assessment of prior probabilities relies
on the doctor’s experience and knowledge. We will look at strategies through which experienced doctors may find some reasonable basis for assigning a prior probability P(H) to plausible
diagnoses.
We will also propose that ‘imaginative deliberation’ enables
clinicians to take actionable steps in diagnosis and treatment. By
imaginative deliberation we mean a process of reasoning through
which doctors ask questions and seek answers in order to select
possible diagnoses and discard others. This process involves inter-
2
Notice, however, that in moving from the right-hand to the left-hand side
of the mathematical equation, an epistemological shift occurs regarding the
facts under study. The patient’s condition, which originally was a fact in
need of explanation – or an explanandum – , becomes evidence to assess
the probability of a hypothesis – that is, that the diagnosis is correct. We
could mark this epistemological distinction by rewriting the equation as
P(H/E’) = [P(E/H)*P(H)]/P(E), where E is a fact regarded as an
explanandum and E’ is the same fact regarded as evidence. To simplify our
discussion, however, in this paper, we will not focus on this epistemological shift from facts regarded as explananda, E, to facts functioning as
evidence, E’.
© 2015 John Wiley & Sons, Ltd.
Selecting clinical diagnoses
D.E. Stanley and D.G. Campos
play between memory and imagination [3].3 Experts in clinical
diagnosis use several strategies to process signs and symptoms,
refine the differential diagnosis – that is, the list of possible or
probable diagnoses that ought to be considered – in order to take
action – for example, further testing, additional family information, biopsy. Medical experience is the link between theoretical
knowing and doing. All attempts to formalize medical thinking
(knowing) have held our interest in developing expert programmes
– for example, DxPlain, Knowledge Coupler, Isabel – but we
maintain that clinical diagnosis and decisions (doing) are too
complex to reduce to artificial intelligence or computer programs
because diagnostic acumen requires the imagination.4
Experienced diagnosis consists in, first, abductive generation
and examination of plausible diagnoses, and then the selection of
diagnoses for further refinement by testing. This is the experimental method of scientific medicine.
This selection often starts from the prior probabilities, P(H),
refined by clinical experience so that an expert’s priors are
conditionalized as P(H’) = P(H/E) and become her ‘informed
priors’. That is, her experience guides and refines ‘naïve or uninformed priors’ – for example, by performing experiments in clinical training to form informed priors. This is what experience
means. In order to arrive at informed priors, though, we need
something that Bayes does not offer. We need to start with our
clinical acumen. We start with our observational assessments (as
was Darwin’s case observing the classification barnacles over an
8-year period or Mendel with the constant differentiating characteristics of peas over a 9-year period) from training in medical
decisions; through these visual assessments, we take our priors, or
they are handed down to us by our teachers, medical journals,
mentors. The Bayesian process is an attempt to update our experience after we have chosen which evidence E supports which
hypothesis H’ and how prevalence – a measure of the rate of
occurrence of a disease in the general population – intersects with
H’.
Medical diagnosis also depends on the transformation of the
patients’ history, physical and laboratory examinations from an
indeterminate array into an actionable strategy: that is, how to
probe the family history, to reassess the physical examination, to
refine testing driven by imaginative deliberation – for example,
could this be a manifestation of a common disease presenting in an
uncommon fashion? Might it even be a disease not yet described?
3
Colapietro writes: ‘Deliberation understood as the transformative interplay between memory and imagination, is the key to understanding reason
in Peirce’s sense; human rationality is, at bottom, the emergent capacity of
imaginative deliberations’ [3].
4
As an anonymous reviewer of this paper noted, in modern medical
education students are instructed in the use of diagnostic algorithms and
even computer-assisted diagnostics, and these are likely to play an important role in clinical practice of these soon-to-be doctors. A discussion of the
interplay of experience and imagination with increasingly powerful automated diagnostic tools would not only desirable but salutary for the future
of clinical practice. However, this issue requires a discussion in itself that
is beyond the scope of this paper. Our conjecture, based on several decades
of experience with the development and attempted application of these
programmes by one of the authors, is that these programmes cannot in
principle and in practice capture the complexity and variety of manifestations of disease. In the end, there will be no substitute for the clinician’s
experienced hypothesis making when confronted with difficult cases.
© 2015 John Wiley & Sons, Ltd.
Having introduced our general thesis, we now proceed to
examine several medical scenarios that exemplify a variety of
strategies doctors use to reduce a large set of possible diagnoses –
the diagnostic space of observation – to a narrower set or even to
a single ‘best’ diagnosis, thus facilitating the choice of a course of
action for treatment. Let us emphasize again that in what follows,
we reconstruct diagnostic reasoning in a variety of cases according
to logical theories (Peircean inquiry, Bayesianism, inference to the
best explanation and so on), rather than provide a fully descriptive
account of how clinicians reason. We acknowledge that clinicians
often do not know the philosophical literature on abduction, inference to the best explanation and so on, and they only sometimes
explicitly use Bayes’ rule. But we hold that these reasoning patterns and strategies are implicit in their reasoning. Making them
explicit will help to enrich the diagnostic training of future
clinicians.
Case no. 1: abductive diagnosis,
deductive consequences and inductive
testing: trochanteric pain syndrome
Clinical details
An 18-year-old female high school student, who plays field
hockey. She complains of pain in the right hip during the early
season of training. Pain is overlying greater trochanter and
involves tensor fascia. Pressure elicits tenderness along lateral
thigh. No drugs, no falls, usual preseason training and observed
normal gait with slight hesitation on right forward step.
Clinician: ‘Initially this falls into the category of a very
common problem: trochanteric pain syndrome/IT band syndrome. There are no ‘red flags’. but the clinician may inquire
about: history of osteoporosis, abnormal menarche, history of
eating disorder, night pain, hip snapping, and perform a
careful physical exam to rule out asymmetry of joint motion
or joint pain. Other possible diagnoses include femoral stress
fracture, referred lumbogenic pain, and coxa saltans (snapping
hip).If the physical examination – testing for full motion, rotation, strength against resistance and elicitation of any pain on
passive movement – and history were negative at this point
the patient would need instruction about trochanteric pain
syndrome/IT band syndrome, and initiate a short course of
therapy and NSAIDS with sports activity modification. Athletic trainer might be advised of diagnosis to plan continuing
preseason training. If there were any suspicion of
intraarticular pain, then an AP pelvis and lateral right hip
radiograph to rule out developmental dysplasia of the hip and
a femoral neck stress fracture. Further imaging could be
obtained based on the results of the plain films (MRI
arthrogram and/or bone scan).’
Logical discussion
The focus of investigation is on the top differential diagnosis: a
syndrome called trochanteric pain. It is the most common cause of
lateral hip discomfort in this age group. The logical form of this
initial abductive diagnosis is
Premise 1: The patient, an 18-year-old female field-hockey
player, presents pain in her right hip, overlying greater trochanter.
3
Selecting clinical diagnoses
Premise 2: If the patient were suffering from trochanteric pain
syndrome, these symptoms would be a matter of course.
Conclusion: There is reason to conjecture that she is suffering
from trochanteric pain syndrome.
Other possible diagnoses, such as femoral stress fracture, are
generated in a similar way and could be entertained.
However, the clinician deduces what other symptoms could be
observed as a result of such a fracture, and observing during
testing that none of them occur (an implicit inductive test), quickly
discards such diagnoses. Note that trochanteric pain syndrome is
selected from the initial list of plausible diagnoses because it
explains all the observed symptoms and frequency statistics
suggest that it is the most likely. As we will see in further cases,
this coincidence of explanatory plausibility and statistical probability is not always present.
The clinician, therefore, here perceives a set of associated symptoms and interprets it as being the effect of a known syndrome.
This interpretation involves an explanation that relies on his
medical knowledge: a general, well-known condition would
explain all of the observed facts. This is called a habitual abduction, that is, one in which the explanation of the observed facts is
already known to the inquiring doctor. It might be likened to
pattern recognition. There is no need for a creative abduction, that
is, one in which the plausible explanation is not yet known and
must be originated by the creative medical scientist [4]. The clinician also identifies tests that would eliminate or confirm alternative diagnoses if additional symptoms – for example,
intraarticular pain – were to surface. This exemplifies, then, the
canonical scenario of a habitual Peircean inquiry: an abductive
hypothesis-diagnosis, well-known to the doctor, would explain all
the observed facts-symptoms; the procedure for observing, or
testing, this hypothesis-diagnosis is already standardized; and
treatment follows the standardized course, unless new, unexpected
facts or symptoms were to emerge.
Case no. 2: progressive elimination
(testing) of abductive hypotheses by
interrogation and imaginative
deliberation: vertebral artery
dissection
Clinical details
Patient:
Clinician:
Patient:
4
‘The room was spinning. I was sweating profusely. I
sat up briefly. I went back to sleep on my back for a
few hours and then woke up. The spinning was less
severe, but it was still there’ [5].
‘When this event started, according to the history,
you had rolled over in bed. Can you describe to me
exactly what happened to you earlier; before the
event began?’
‘I had center court seats for the New York Open and
I was there every day. I generally practice serving
and volleying 5 days a week; last month it was every
day. The day before the dizziness, I played six sets
of tennis before my coach had me practice serving
for 90 minutes. I needed to increase my serve velocity by reaching higher and higher and arching my
neck even further to get above the ball.’
D.E. Stanley and D.G. Campos
‘This could be due to vestibular hydrops (fluid in
Clinician
(to herself): inner ear), Meniere’s disease, otolithic (inner ear)
disturbance, toxins.’
Clinician: ‘I understand that you do a lot of vigorous exercise,
including competitive tennis at the “semipro
level.” Had you done anything unusual before this
episode?’
Logical discussion
In many situations, the abductive generation and selection of
diagnoses for further testing and eventual treatment occurs
sequentially. As the clinician conjectures and then discards possible causes to explain the patient’s symptoms, she seeks further
information from the patient and deliberates with herself, until
finding a precise hypothesis that explains all the symptoms. This
case illustrates that sequential diagnostic process in the form of
a dialogue between clinician and patient and the clinician’s own
deliberation.
At this point, the clinician has begun to generate plausible
hypotheses, such as fluid in the inner ear or Meniere’s disease. Any
of these would explain the symptoms. But the list of possibilities is
too long already; it would be uneconomical and impractical to test
them all, and there is not enough information to eliminate any of
them. So she proffers further questions to narrow the diagnostic
space, eliminating some hypotheses while refining others.
Further clinical details
Clinician (to herself reviewing the patient’s history): ‘The
patient reports lying in bed, noted vertigo, diaphoresis (sweating), and the development of progressive and fixed symptoms,
including tilting to the left, nausea, vomiting, headache,
oscillopsia (the visual perception of objects moving when
they are actually stationary), and changes in facial sensation.
The first consideration, the most common, is vestibular origin
secondary to a viral inflammation. But there were no reported
upper respiratory symptoms; this might eliminate the most
common etiology.
I must construct a chronology that might account for the
patient’s reports. Neurologic symptoms can be localized to
the central nervous system or the peripheral nervous system;
most of the signs and symptoms in this case are of central
origin. But I must also consider rare diseases, e.g. an inherited
late manifesting storage disease, or late onset of muscular
dystrophy. Further history and testing might eliminate a rare
cause; but these entities must be kept in the diagnostic space
until a commoner, more plausible explanation arises.’
Logical discussion
The clinician, by interviewing the patient, searches for historical
clues in order to discard some of the initial possible diagnoses
under consideration or perhaps generate a new plausible diagnosis
not yet considered. In this case, the absence of upper respiratory
symptoms discards – or at least demotes in the order of possible
hypotheses – the most frequent diagnosis. She is trying. Thus, she
tries to identify ‘clinical pearls’ and ‘red flags’ – that is, facts that
© 2015 John Wiley & Sons, Ltd.
Selecting clinical diagnoses
D.E. Stanley and D.G. Campos
may suggest a salient hypothesis worth investigating. She
rehearses the salient reports from the patient.
At this point, she confronts a set of facts that need to be
explained, economically, by a single diagnosis. The clinician asks
whether any one explanatory hypothesis can explain all of these
symptoms. She is seeking for a hypothesis that captures and amplifies the symptomatology even though the hypothesis may at first
consideration seem implausible. If true, however, it would
elegantly explain the clinical picture.
While she would like to have one unifying diagnosis, she knows
that sometimes this is not the case. There may be two or three
different diagnoses, one hiding another. The image of a Russian
doll may illustrate this situation.
Next, the doctor’s reasoning strategy will be further refined by
imagining the results of the physiological and anatomic descriptions. She will ponder the basic anatomic structures that may
account for a richer hypothesis.
Though it is infrequent, the problem may be a dissection of a
vertebral artery. Even if it is an a priori unlikely hypothesis, it
would be consistent with the patient’s reported activities and it
would explain all of the symptoms.
In this scenario, then, we see that the generation and selection of
plausible hypotheses is often sequential and many are kept in the
space of possible differential diagnoses. It is important to rank
them. Frequency, or a priori likeliness, is an initial criterion but it
is often trumped by the facts of the case. The doctor then must
appeal to her imagination and observation of the crucial facts to
link them in a way that may lead to an a priori unlikely but
simplifying diagnosis.
Case no. 3: Peircean inquiry –
abductive-deductive-inductive
reasoning, with modelling and
economic considerations: skin lesions
that look alike
Further clinical details
Clinician: ‘First, I will try to identify the anatomical localization
and cause of the present illness from the history provided.’ (summarizing for herself): ‘The patient is a
49-year-old, left-handed avid semi-pro tennis player
who, after rolling onto his stomach . . . ’
She thinks constructively. If this were a stroke then the
common precursors e.g. hypertension, diabetes, atrial fibrillation should be present. If not, still what if an ischemic
event accounts for this congeries? The clinician narrows the
diagnostic list by considering less common etiologies; she
ranks ischemia over inflammation even though this is a
young patient. She ventures it may be a vascular anomaly, a
ruptured aneurysm, even an arterial dissection brought on
by the vigorous exercise especially the exaggerated
hyperextended neck movements during tennis match observations and by practicing serving the ball by ‘getting above
the toss to change the trajectory across the net.’
Diagnosis: dissection of vertebral artery.
Logical discussion
Here, the strategy is to put hypotheses in an ordinal fashion in
order to proceed efficiently. The experienced clinician will invoke
Ockham’s razor – a maxim to seek parsimony in explanatory
hypotheses by eliminating unnecessary elements from them – as
often as she can. For instance, she demotes, in the ordinal rank of
plausible diagnoses, the hypothesis of a stroke because the precursors are not present in the young athlete.
In this case, the discussant is searching the clinical landscape
for markers to identify the most efficient strategy to pursue in
order to make a diagnosis. She is interrogating her own experience, imagination and clinical acumen to link parts of the
history, tests and infer a tentative explanation. She considers a
couple of possible hypotheses and ranks them according to prior
probability – ischaemia over inflammation – but as she imagines
the athlete’s physical activities before the onset of symptoms,
she focuses her attention on an important fact, namely, the ‘exaggerated hyperextended neck movements’. This leads to insight.
© 2015 John Wiley & Sons, Ltd.
Clinical details
Clinician: ‘This woman presented with recurring painful erosions and with mucosal involvement during one episode. Her
condition responded quickly to prednisone. Laboratory testing
was positive for multiple markers of rheumatologic diseases
as well as history of NSAID use for pain. Three skin biopsies
were performed, and examination of each specimen was suggestive of a different diagnosis – erythema multiforme, toxic
epidermal necrolysis, and Stevens-Johnson syndrome’ [6].
Logical discussion
The first stage in this inquiry is abductive – at least three possible
differential diagnoses are suggested in order to explain the
observed signs and symptoms. These are all generated from the
doctor’s habitual background knowledge. He knows, for example,
that if the patient had erythema multiforme, her observed symptoms would result. But at least two other plausible causes must
also be considered.
His reasoning could continue by invoking each hypothetical
cause, deducing observable and testable consequences if the
hypothesis were true, and testing the hypothetical consequences
against the clinical history and laboratory findings and therapy. In
terms of the Peircean triadic account of inquiry, this is the
abduction-deduction-induction sequence.
To simplify the scenario, she needs a strategy to narrow down
the diagnostic space, one which relies on her clinical acumen. For
this purpose, the presence of markers of rheumatologic disease and
ingested drugs is foremost in the clinician’s mind. These are
important clues to a unifying and simplifying hypothesis.
Further clinical details
The next step is to have another review of these three biopsies.
This would be standard procedure when controversial interpretations are in the medical chart. This appeal to retrospection is very
important.
As the nosology of painful papulo-nodular skin lesions is extensive, a narrowing criterion – outside of, and supporting, the histo5
Selecting clinical diagnoses
logic diagnosis – is required, while at the same time, an assessment
of the threat to the patient is driving a swift hypothetical approach.
So the common denominator would be, most probably, a result
of an autoimmune phenomenon; namely, the presence of
rheumatologic markers in the patient’s serum. First, treat with
steroids to prevent life-threatening disease such as toxic epidermal
necrolysis, Staphylococcus scalded skin syndrome, etc. This is the
primary move, as the latter two diagnoses are possibly life threatening and require emergent therapy, even before a firm
histopathologic (microscopic skin biopsy) diagnosis can be established. If the emergency therapy does not show marked improvement, then a biopsy and serologic repeat tests for an immunemediate skin ulcer would be indicated (the first tests have been
falsely positive).
Clinician (to herself): ‘I already have a hunch that this is an
immunologic response ( the underlying etiology) to anti-self
antibodies to skin cells. I should be prepared to keep the positive immunologic serum markers as the possible “trump card.”
I might ask for the biopsies to be stained for: immunoglobulins IgG, to objectively show that immunoglobulins are the
cause of this skin disease. Done expeditiously, the diagnosis
would be to treat the systemic skin disease (autoimmune, e.g.
lupus erythrmatosus), predicting that the lesions will resolve.’
Logical discussion
The clinician, relying on her knowledge and experience, identifies
a fact (pre-existing immunologic disease) that helps to narrow the
diagnostic space. This fact would most frequently be explained by
an autoimmune disease. This moves her strongly in the direction of
diagnosing and treating immunologic manifestation in the skin of
a systemic immunologic disease. She has in fact arrived at an
abductive model, informed by medical theory, of the patient’s
overall condition. This means that the clinician now has created an
overarching schematic or diagrammatic understanding of the relations between the patient’s clinical history (pre-existing condition)
and current signs and symptoms, on the one hand, and the conjectured causes on the other.5
Specifically, the reasoning to correctly diagnose skin biopsies
and the patient’s presentation of signs and symptoms involves:
1 A histologic theory of bullous skin lesions – where the separation of the skin layers occurs, for example, intra-corneal,
intraepidermal, sub-basalar, subepidermal;
5
We will not delve into the nature and function of these schematic diagrams or models created by the doctor’s imagination. Let us briefly say that
the ‘imagination’ is the reasoning ability that allows her to transform
complex facts and relations into simplifying signs or ‘diagrams’. such as in
schemata and figures, for further investigation. A ‘diagram’ is a sign,
whether drawn or only imagined, that presents all the relevant facts and
embodies their relations [14,15]. The clinical diagram suggests explanatory relations between observed effects-symptoms and possible causes
(aetiologies) of diseases. The clinician investigates whether the schematic
picture adequately represents the incorporated elements, that is, facts and
relations such as symptoms, diseases and causal relations. Are the elements
consistent, in medical terms, with one another and with the suspect
disease? Does consistency with the imagined schema of symptoms and
disease affect the incorporation of pathophysiology and the speaker’s
words, especially those that are selected by the hearer/medical person? For
further discussion of the semiotics of medical practice, see Silveira [16].
6
D.E. Stanley and D.G. Campos
2 Observation – the clinical examination, attention to the distribution and character of these lesions may allow a single pathologic
diagnosis;
3 Creating a model (the probable natural history) of the disease
while using additional clinical data (immunologic markers) to
inform and refine the model, while maintaining an urgency to
avoid serious complications – for example, treating an infectious
disease with steroids, or not giving steroids where indicated, and
4 Basing further definitive testing or even treatment on the theoretical model that fits the presentation.
Note, however, that beyond theoretical modelling that would
explain abductively the patient’s condition, the clinician must keep
other criteria for diagnosis and treatment in mind. The foremost
additional consideration is ‘economy’ in diagnosis – in this case,
economy of time. Two of the possible diagnoses are lifethreatening, so the doctor must be swift to prescribe treatment
while searching for the correct diagnosis. This is in line with
Peirce’s procedural logic of abduction for narrowing down a vast
space of generated plausible hypotheses. One criterion is to ascertain which hypotheses are recommended for testing by the
economy of research, that is, economy of money, time, thought
and energy [7].
In this respect, this scenario also provides a curtailed example of
what Neurath suggested last century: we can make adjustments to
the theory when inconsistencies arise, or we can accommodate the
theory by adding more clinical data, but we must proceed to the
remedy without exhaustive and extensive testing – this may be an
emergency! There are more than 25 dermatologic conditions that
may be confused with one another! They are look-alikes and can
masquerade. They are identical for the viewer. We do not have the
time to start with parts, we must treat the whole, and expediently:
the ship is on sea! [8].6
Clinical conclusion: ‘She is therefore required to take into
account the previous skin biopsies interpretations and, since
the histologic features overlap in multiple skin diseases, to
use additional criteria – e.g. constitutional symptoms, systematic findings, history of drug ingestion – in order to specifically identify which of the three histologic findings is the
correct one, or better fits the larger clinical picture; and this
must be done rapidly. Look-alikes are common and test the
experience of the clinician to recognize an emergency. There
may be no time to biopsy. She must know more about the
disease in order to sift out the most likely disease. She must
understand and incorporate which etiologies (causes) would
eventuate in these skin manifestations; she would need to
exclude infectious, malignant, parasitic, and allergic (except
to her own skin constituents!) ones. But she is using her clinical experience to decide this is indeed an emergency.’
6
Neurath’s point is primarily about the language of science, but we are
adapting it to our needs in accounting for the logic of diagnosis. In his
words: ‘There is no way to establish fully secured, neat protocol statements
as starting points of the sciences. There is no tabula rasa. We are like
sailors who have to rebuild their ship on the open sea, without ever being
able to dismantle it in dry-dock and reconstructed from its best components. Only metaphysics can disappear without a trace. Imprecise ‘verbal
clusters’ [Ballungen] are somehow always part of the ship. If imprecision
is diminished at one place, it may well reappear at another place to a
stronger degree’ [8].
© 2015 John Wiley & Sons, Ltd.
Selecting clinical diagnoses
D.E. Stanley and D.G. Campos
Case no. 4: red flags, experience and
informed Bayesian probabilities: viral
lymphadenopathy
Clinical details
A 36-year-old-man, previously in good health, experiences
fatigue, mild weight loss and palpable lymph nodes in the groin,
the axilla and the anterior and posterior neck. He had returned
from a business trip to Asia 3 weeks earlier. The lymph nodes are
firm, but movable. There is no node larger than about 1.0 cm. A
chest X-ray shows multiple enlarged lymph nodes of similar size
in the mediastinum and retroperitoneal area. He has low-grade
fever of 38°C. He does not use illicit drugs, does not smoke and is
a graduate student in philosophy. Before he travelled on business,
he received vaccinations against several tropical diseases as well
as a skin test for tuberculosis [9].
Clinician building a diagnostic strategy: ‘Whenever we listen
to the history of an illness and the case presentation, whether
they are being described by another physician or the patient,
certain words and phrases seem to carry more weight than
others, and experts are trained to listen for these words pregnant with potential truth: words of uberty. Why is this choice
the case for some clinicians and not for others? In this case,
the initial impression was dominated by the patient’s young
age and previous good health, the brief duration of his present
illness, his recent travel to Indonesia, the low grade fever and
enlarged lymph nodes.’
The task is to construct a differential diagnosis around the
development of lymphadenopathy and then narrow the list on the
basis of what we know about this patient.
Logical observation
Initially, a doctor could abduce several possible causes of lymph
node enlargement, such as early HIV infection, reaction to a medication, exposure to an allergen, early lymphoma, systemic lupus
erythematosus, infectious mononucleosis, Castleman’s disease,
Kimura disease or Rosai–Dorfman disease. There are numerous
possible abductive diagnoses that flash into a clinician’s mind, so
it is necessary to narrow the list down by looking for critical facts
or ‘red flags’, that is, facts that may eliminate some possible
hypotheses while making others salient for further examination.
Further clinical details
In this case, the patient is a young man, with few risk factors, who
has a monotonous pattern of small, firm, but movable lymph
nodes. This would point to a systemic rather than to a local disease.
His mild symptoms also rule against metastatic cancer, lymphoma,
and is suggestive of a reactive process. Therefore, the clinician
chooses a research pathway that would put reactive lymph node
enlargement high on the list of plausible diagnoses worthy of
further testing. Regarding other possible initial abductive hypotheses, reactive lymph nodes of this size and distribution would not
suggest some lymph node diseases, for example, Kimura, Rosai–
Dorfman or Castleman’s. The symptoms are understood as being
compatible with some possible hypotheses but not with others.
© 2015 John Wiley & Sons, Ltd.
Logical discussion
The clinician identified some facts that quickly eliminated a series
of possible hypotheses from the space of differential diagnoses
deserving further scrutiny. This is an important step, but the
inquiry requires further strategic moves.
There may be associated probabilistic considerations to assess
whether a diagnosis ought to be selected to inform a future course
of action. Recall the formulation of Bayes’ rule to estimate conditional probabilities:
P ( H E ) = [ P ( E H ) × P ( H )] P ( E )
If doctors reason along Bayesian lines as they look for red flags,
they have learned from clinical experience to posit the prior probabilities of diagnoses-hypotheses, P(H), that clinical training has
conditionalized by experience. The beginner with little experience
in the practice of medicine posits priors from her training. As she
gains experience, the priors, having been tested, are refined and
become ‘informed priors’ that more likely fit the diagnostic space
and are then utilized as priors. These priors are empirically based
rather than merely subjective in part because past frequencies are
reliable for common diseases. This is the contribution of observed
frequency data; the education of the clinician is modelled on her
exposure to frequencies of occurrence.
For illustrative purposes, we may say that an inexperienced
clinician posits a P(H) informed by her training, but an experienced clinician posits a P(H)’ informed not only by training but by
her developed acumen. In other words, from exposure to history,
signs and symptoms similar to the case encountered, experienced
clinicians are able to recall how strongly, if at all, their prior
suspicion H, plus the evidence E, lead to a high P(H/E). And these
conditionalized priors, P(H)’ = P(H/E), are used when iteration of
the signs and symptoms, E, and the prevalence of the conjectured
disease that causes them, P(H), are considered in new cases. In
clinical practice, P(H) is estimated by the prevalence of a disease.
Prevalence is the rate for a disease that is equal to the number of
patients per 100 000 persons per year in the population who have
the disease at the time of the study; in other words, it measures the
rate of occurrence of the disease in the general population.7
The experienced doctor is able to estimate this initial assessment
of P(H) in the specific clinical situation, geography and to consider
the environmental exposure under investigation. We claim this
ability is what experienced clinicians possess from exposure to the
congeries of signs and symptoms, or evidence, inserted into the
probability estimate.
Travel to Indonesia may change the likelihood of the considered
priors because the evidence is strengthened and complemented by
the foreign exposure; thus, the prior is shifted from that without
this exposure, P(H), to that with exposure, P(H)’. In terms of this
specific scenario, the initial red flag is the homogeneous size of the
lymph nodes. But the clinician has further considerations.
She pauses for reflection and allows for re-estimation of the
prevalence and re-evaluation of exposure to foreign travel as part
of the evidence. The pause from closure is an index of the reflec7
Prevalence should be distinguished from incidence; the difference is time
dependency. Incidence is the number of patients with the disease per
100 000 people per year. So the prevalence of a disease like the flu may be
high during the ‘flu season’, but the incidence may be low during the
ensuing year.
7
Selecting clinical diagnoses
tive mode of generating and selecting hypotheses. The clinician
asks herself, reflexively, what could I be missing? What other
patterns would fit these congeries? Exposure during foreign travel
becomes relevant. By attending to this fact, she is able to adjust the
probabilities she assigns to possible diagnoses, giving higher priority for further investigation to one or a few hypotheses, and
discarding others as improbable given the two ‘red flags’ – size of
nodes and foreign travel.
This is part of learning from experience. In this case, the clinical
discussant structures the discussion on signs and history – lymphadenopathy, exposure to tropical diseases – and from recall is
able to simplify the discussion because her experience has shown
her that lymph nodes smaller than 1.0 cm are probably reactive
rather than neoplastic – that is, superficial and deep seated lymph
nodes that are similar in size and even consistency suggest a
reactive pattern rather than a malignant disease.
She finally suggests a diagnosis – either Kikuchi-Fujimoto
disease or miliary tuberculosis – based on two red flags: size of
lymph nodes and travel to Indonesia. Here, the foreign travel
narrows the differential diagnosis as it shifts the prevalence, at
least on the first examination; yet biopsy may be indicated if the
doctor is not satisfied to proceed to treatment. She must also
tolerate confounders and keep her vision open to other inputs,
since some cases of infectious disease – for example, Epstein–Barr
virus – may cause lymphadenopathy larger than 1 cm.
Case no. 5: statistics (frequentist and
Bayesian) and economy: man with
mid-back pain
Clinical details
A 57-year-old man with 3-month history of mid-back pain, nonradiating. Location: T12-L3, tender to palpation. He does not
perform any daily physical exercises. The condition occurred once
in his early 40s. He describes it as aching and worse standing.
NSAIDs are of little help. He is a non-smoker and occasional
alcohol drinker. He works as a mid-level manager in an insurance
company. Routine visits to primary care doctor: normal CBC,
plain radiograph of lumbosacral area shows no bony pathology.
This falls into the category of back pain that has not resolved in
the usual 4–6 weeks as would be expected if this were a case of
non-specific lower back pain. In the scenario above, there are no
‘red flags’, but the clinician may inquire about area of spine represented in radiograph – especially since many lumbar spine radiographs do not contain thoracic vertebrae (metastatic pancreas
cancer does involve lower thoracic vertebrae). He may also inquire
about spine trauma, history of osteoporosis, history of cancer, fever,
chills, weight loss, illicit drug use, immunosuppression, night pain
and do a careful neurological exam. If all of these were normal, only
then would he consider screening with bone scan (to rule out
metastases and insufficiency fracture), laboratory tests (CRP and
ESR to rule out occult infection, rheumatologic disorder and serum
protein electrophoresis to rule out multiple myeloma). In the background of his clinical experience, lower back pain of unknown
aetiology is the most common diagnosis; yet, he maintains an open
question, as the history is somewhat longer than he would expect.
The patient is slightly older but his inactive physical state is still
consistent. His differential diagnosis space remains open.
8
D.E. Stanley and D.G. Campos
If the first examination were normal, additional tests could be
obtained serially to minimize cost. In this case, the clinician wishes
to perform the test that rapidly yields the most information about a
condition that is very dangerous, namely, he wants to rule out cancer
or myeloma. At this age, both conditions must be ruled out.
Logical discussion
The clinician is looking first for ‘red flag’ signs or symptoms,
which guide the investigation. He finds none beyond the 3-monthlong history, and the patient’s age. He then estimates the prevalence of this condition given the setting of his practice. As defined
before, prevalence is the number of patients per 100 000 persons
per year in the population who have the disease. The clinician
recalls that regardless of the sensitivity and specificity of further
testing, prevalence (the tacit hypothesis) is heavily weighted in any
Bayesian calculation. For a test
Predictive value = (Prevalence )(Sensitivity )
[(Prevalence ) (Sensitivity )
+ (1 − Prevalence ) (1 − Specificity )],
8
where the specificity is the true negative rate or the proportion of
actual negative cases that are ruled out, and the sensitivity is the
true positive rate or the proportion of actual positive cases that are
ruled in .
These measures are highly dependent on the prevalence of the
disease in the population undergoing testing. Alternatively one can
calculate the likelihood ratio (LR), which does not depend on
prevalence. This is frequently advocated:
For a positive test result, LR + = Sensitivity (1-Specificity) .
For negative test result, LR − = (1 – Sensitivity ) Specificity .
9
Unfortunately, these values are not readily generalizable.10
Observing no red flags, the doctor proceeds to try to rule in or
rule out the pathologies he considers likely, and this is based on the
prevalence of this condition in his clinical practice. He stratifies
the investigation based on the urgency of the implications of the
possible aetiologies. He wishes to obtain data serially in order to
minimize cost. In this sense, he follows the criterion of economy
identified by Peirce – that is, according to the logic of abduction
that scientists apply in practice, one of the criteria for selecting
among tentative hypotheses is the cost of testing them (Peirce, CP
5:600; n.d.).11 This dictum is frequently avoided most likely
because of medico-legal implications.
However, the doctor also stratifies testing by seriousness, for
example, myeloma and metastatic cancers are higher in the possible diagnostic list because of history and age and are dangerous.
8
Note how prevalence affects the calculation of predictive value. The
prevalence of non-specific lower back pain is high in his office setting.
9
Alternatively, from a 2 × 2 chart of true positives (TP), false positives
(FP), true negatives (TN) and false negatives (FN): the predictive value of
a positive test is 1[TP/(TP+FP)] × 100, while the predictive value of a
negative test is [(TN/TN+FN)] × 100.
10
For a thorough presentation of prevalence and predictive value, see
Galen and Bambino [17], pp. 167–264.
11
Following standard practice in Peirce scholarship, references to Peirce’s
Collected Papers are abbreviated CP followed by volume and paragraph
number and date of drafting or publication.
© 2015 John Wiley & Sons, Ltd.
Selecting clinical diagnoses
D.E. Stanley and D.G. Campos
He pauses, perhaps, because the likelihood of these hypotheses is
not the highest on an ordinal scale, but these possibilities are the
most serious considerations.
Case no. 6: inference to the loveliest
explanation: Nigerian woman with
melena secondary to infectious
gastric ulcer
Clinical details
Discussant: ‘When I first evaluated this 55-year-old Nigerian
woman, I thought that identification of the source of melena
would quickly lead us to the diagnosis. Melena is typically
caused by hemorrhage in the upper gastrointestinal tract,
proximal to the ligament of Treitz; however, blood loss in the
distal small bowel or proximal colon may also result in
melena’ [10].
Logical discussion
Here we find the first movement to form a hypothesis, except in
this case, the discussant is prompted to entertain a different
hypothesis. She has been made aware of the correct diagnosis. The
logical situation could be described as follows.
The clinician initially generates two possible abductive explanations for melena: haemorrhage in the upper gastrointestinal
track or blood loss in lower tract. She also immediately ranks these
two possible diagnoses. The typical site is upper gastrointestinal,
and so she assigns it a higher prior probability of being the correct
diagnosis, but she also keeps in view the possibility of blood loss
in the colon, though with a lower prior probability. The clinician,
then, has chosen prevalence as the promising path to a hypothesis,
but she must be careful about relying exclusively or hastily on
prior probabilities.
Recall the adage of hoof beats in the office: ‘horse, not zebra’.
It is helpful but most be pondered in context. In this case, zebras
are still in the running. Closure on diagnostic considerations
should not occur too early or too late – this requires a balancing act
when efficiency does not jeopardize the well-being of the patients.
This adage, very well known in medicine, may be illustrated as
follows. In Costa Rica, there is only one form of malaria: Plasmodium vivax, the ‘horse’ so to speak. But a doctor there who was
concerned about cerebral malaria of the P. falciparum variety – the
‘zebra’ – would have to be very certain that the patient had not
foreign travel outside Costa Rica and no blood transfusions to
exculpate the notion of a diagnosis of cerebral malaria. But in
Nigeria the presence of P. falciparum exists. Experience in one
setting does not allow for easy extrapolation to other settings; this
is the limitation of prevalence estimation and thus likelihood.
Further clinical details
In evaluating this patient, the clinician first considered esophageal
causes of melena, including drug-induced esophagitis, reflux
esophagitis, a Mallory–Weiss tear and cancer. She also considered
causes originating in the stomach and small intestine, including
peptic ulcer disease, gastritis.
Further details of her history were helpful: this woman had
autoimmune hepatitis and cirrhosis treated with steroids, and dia-
© 2015 John Wiley & Sons, Ltd.
betes mellitus, type II. Medications included prednisone, insulin,
furosemide and omeprazole. Thus, knowledge of her national
origins, her medical history and condition required further
investigation.
Logical discussion
The discussant is trying to estimate causal prevalence based on her
clinical training and experience – the likelihood that origin of
melena would lead to a conclusive diagnosis. However, this
approach does not take into account the previous history (liver
disease, diabetes, national origin, steroid treatment) and does not
further identify the site of bleeding. As a result, the clinician has to
reassess and perhaps re-interview the patient. Thus, she invokes
other considerations and offers a resultant, alternative hypothesis.
Final clinical details
With prolonged immunosuppression (prednisone) for the liver
disease and diabetes, she was exposed to opportunistic infections.
The history of melena and probable upper gastrointestinal source of
bleeding suggested the need for an upper endoscopy that showed a
gastric ulcer – the diagnosis is based on a biopsy showing inflamed
gastric wall with fungal hyphae. Reviewing the patient’s history, the
clinician recalls she was from Nigeria where parasitic infestations
such as Strongyloides, malaria and infectious aetiologies are more
common, especially in this immunocompromised woman.
Logical conclusion
This exercise is a demonstration of the clinician’s ability to investigate one sign of illness (melena) while maintaining the possibility of a zebra. Thinking about the presentation to include her
history and country of origin allows her to avoid focusing too early
on the initial complaint. The previous medical history of autoimmune liver disease under immunosuppressive treatment brings up
a differential diagnosis in which zebras are still on the run. Taking
into account the present illness past history and medications, she
pursues a different hypothesis because, she suspects, it will lead to
a different diagnostic pathway and eliminate excessive, perhaps
redundant, investigations.
In terms of the logic of hypothesis selection, in this case, considerations of a priori ‘likeliness’ do not suffice to select the correct
diagnosis, given the patient’s origin and past medical history. She
sifts the medical history and current illness to search more specifically for a hypothesis that provides a ‘lovely’ explanation that
encompasses both past and present illness – that is, an explanation
that will take into account all of the patient’s illnesses (Lipton [11],
pp. 53–70). Lovely diagnoses are parsimonious – Ockham’s diagnoses – while unifying all observed symptoms and conditions into
a powerful, elegant explanation. This view of diagnostic loveliness
links simplicity to explanatory power and beauty. In fact, sometimes
a clinician will exclaim: ‘What a beautiful diagnosis! It puts
together all of the unexplained and unexpected details into a composite picture that portrays exactly what we can now understand and
treat’. The sort of loveliness we are suggesting is not a pseudoaesthetic criterion, but rather an epistemological concept.
In assessing the logical diagnostic situation, we are following
Peter Lipton who distinguishes between the inference to the likeliest and inference to the loveliest explanations. Given the empiri9
Selecting clinical diagnoses
cal evidence, the likeliest explanation is ‘the explanation that is
most warranted’ by inductive testing, while the loveliest explanation is ‘the one which would, if correct, be the most explanatory or
provide the most understanding’ [12]. He writes, ‘likeliness speaks
of truth; loveliness of potential understanding’ [12]. In this specific
medical scenario, the selected diagnosis was not the most a priori
likely in terms of prevalence; however, it yields the best potential
understanding of the patient’s condition. It explains all the symptoms and facts of the patient’s medical history in a simple, unified,
and causally connected way. It explains clearly which causes led to
which effects and why, and it does not leave relevant facts of the
patient’s condition or medical history out of account. In short, the
diagnosis offers a lovely explanation.
Lipton also argues that the loveliness of a hypothesis may function, in the logic of hypothesis selection, as a surrogate for its
likelihood [11]. In the medical scenario under analysis here, the
loveliness of the more carefully considered diagnosis – melena
secondary to an infectious gastric ulcer – indicates that its
informed prior probability trumps the prior probability of the first
hypothesis – gastrointestinal bleeding – which relied on prevalence (probabilities) alone.
This scenario illustrates, then, that diagnostic closure cannot
come too early in the process – several simultaneous, potentially
conflicting, hypotheses are often under consideration and the
experienced clinician requires acumen to choose a diagnosticselection strategy from a wide variety of possible approaches.
Summary
The foregoing scenarios are illustrative simplifications of the diagnostic process that is messy and does not follow strict rules. Yet,
they exemplify a variety of logical strategies for selecting differential diagnoses for further testing and treatment from an
abductively generated space of plausible diagnoses. In actual practice, these selective strategies may be used sometimes singly, more
often in groups or even seriatim, to choose a diagnosis.
The major anchor in establishing a diagnosis is insightful,
knowledgeable perception of details that, fitting into a plausible
picture, allows for tentative resolution and action. By this, we
mean that both experienced perspicacity and scientific knowledge
are important to effective diagnosis. These capacities allow the
doctor to generate plausible diagnoses and select the most worthy
ones for testing and treatment. Sometimes even a minor detail – an
inadvertent gesture; a change in voice or speech; the mood of the
patient; a fact such as foreign travel mentioned in passing while
reviewing the history, or family history, which in the future should
receive even more attention as genetics will probably play a larger
role in ‘precision medicine’ – can lead the observer in a different
direction for diagnosing. A plausible diagnosis that at first may
seem more prevalent or likely may become a less viable option to
the perspicacious, experienced clinician.
Thus, in order to turn insightful, knowledgeable perception into
effective diagnosis, the doctor should have recourse to a diverse kit
of logical strategies to choose from the space of plausible diagnoses. As the preceding medical cases show, these selective strategies include Peircean triadic inquiry – abduction, deduction and
induction – frequentist statistical inference, Bayesian inference
relying on prior probabilities informed by experience, inference to
the loveliest explanation and so on. Under every strategy, imagi-
10
D.E. Stanley and D.G. Campos
native deliberation sharpened by clinical experience is explicitly or
implicitly at work. We summarize our discussion, then, by suggesting this dictum: ‘Our best guesses are winnowed by the wind
of experience’.
Acknowledgement
We would like to acknowledge the assistance of Librarians at
Maine Medical Center, especial thanks to Amy Moore for editing
footnotes.
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