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Record W2081042077 · doi:10.1097/acm.0b013e31827b258d

Clinical Decision Making

2013· letter· en· W2081042077 on OpenAlexaboutno aff
Pat Croskerry, Gordon Tait

Bibliographic record

VenueAcademic Medicine · 2013
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsnot available
Fundersnot available
KeywordsMedical diagnosisCognitionPsychologyCognitive psychologyInterpretation (philosophy)Computer scienceMedicinePsychiatry

Abstract

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To the Editor: In their study of diagnostic reasoning in medical graduates, Sherbino et al1 have shown that diagnostic accuracy is inversely related to the speed of determining the diagnosis. They conclude that this challenges the dominant view in cognitive psychology that rapid intuitive reasoning is prone to error. However, we believe that these results are consistent with current models of clinical reasoning, in which decision making involves a combination of System 1 (intuitive) and System 2 (analytic) thinking and in which intuitive decisions are more vulnerable to error than those made in the analytic system. An alternative interpretation of the study is that the time needed to solve the diagnostic problem—under the experimental conditions used by Sherbino et al—is simply a measure of problem difficulty, which is a reflection of the study participant’s knowledge base. For any problem that involves System 2 decision making, the finding that error is associated with a longer response time is not surprising. The exercises in the authors’ study are similar to those provided in exams, and it is likely that the participants primarily used System 2 in arriving at their diagnoses, as they would in exams. The relationship between the participants’ accuracy and their exam scores confirms this, and the authors state that “the longest RT [response time] was associated with the most difficult case (as judged by overall cohort accuracy).” Requiring a response within one minute does not necessarily cause participants to use System 1 in solving the cases, and faster responses are likely to occur when the participants have the knowledge base to quickly arrive at the correct conclusion using System 2. We know that System 2 reasoning is not error-free, especially with residents in training, and a System 2 failure is likely to be due to a deficiency in knowledge. Because all pertinent findings were provided to the participants, the exercises in the authors’ experiment do not reflect the actual complex processes involved in patient assessment, where clinical decisions are made regarding which findings are pertinent for ruling diagnoses in or out. This type of study highlights the difficulty of conducting research that yields findings that can be meaningfully applied to the real world of clinical medicine, where context, patient factors, ambient conditions, human factors, team dynamics, and a variety of other influences prevail. It is likely through those complex processes that the majority of System 1 errors occur, and it would be dangerous to promote the notion to either trainees or practicing clinicians that speed increases accuracy. The bottom line remains that in clinical practice, more errors are associated with System 1 than with System 2 thinking. Further, System 1 does not typically result in error; many intuitive decisions are correct, especially those made by experienced clinicians—System 1 thinking works most of the time. Of course, being right most of the time, no matter which system of thinking is used, is not acceptable in clinical medicine. Research to gain a deeper understanding of diagnostic processes in actual clinical situations may help us find ways to increase the rate of accurate diagnoses. Pat Croskerry, MD, PhD Professor, Department of Emergency Medicine, and director, Critical Thinking Program, Division of Medical Education, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; [email protected] Gordon Tait, PhD Assistant professor, Departments of Surgery and Anesthesia, University of Toronto Faculty of Medicine, and staff scientist, Department of Anesthesia and Pain Management, Toronto General Hospital, Toronto, Ontario, Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.074
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0060.004
Open science0.0040.003
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0370.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.456
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

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