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Record W2320565199 · doi:10.1097/acm.0b013e31828a3d7f

Clinical Decision Making, Fast and Slow

2013· letter· en· W2320565199 on OpenAlexaffabout
David Petrie, Sam Campbell

Bibliographic record

VenueAcademic Medicine · 2013
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPremiseFallacyVariable (mathematics)Statement (logic)Interpretation (philosophy)Cognitive psychologyAnticipation (artificial intelligence)Computer scienceCategorical variableCategorizationEpistemologyPsychologyConfirmation biasSocial psychologyArtificial intelligenceMachine learningPhilosophyMathematics

Abstract

fetched live from OpenAlex

To the Editor: We read with anticipation Sherbino and colleagues’1 recent article. However, we find it ironic that there are some cognitive shortcuts made in the authors’ premise, analysis, and conclusion that may limit the practical implications of their study. The premise asserts that “according to the literature, diagnostic errors arise primarily from System 1 reasoning, and therefore they are associated with rapid diagnosis.”1 This interpretation of the literature commits what is referred to as “the round trip fallacy”2 (e.g., no evidence of disease means that there is evidence of no disease). The statement that System 1 reasoning is more error prone (which we believe the literature does support) does not necessarily lead to the round-trip assumption that errors in diagnostic reasoning primarily arise from System 1. The authors correctly point out that there are many other potential reasons for errors. Their premise also conflates the concept of System 1 reasoning with diagnostic error and rapid time, which we do not believe is consistent with the descriptions of System 1 and System 2 thinking by Kahneman and others.3-5 A possibly erroneous assumption in the analysis is that a categorical variable (System 1 versus System 2) can be directly equated with a continuous variable (time). Fast(er), in relative terms, does not necessarily confer a change in categories of reasoning. Therefore, unless an answer is immediate (not just relatively faster), and prior to our awareness,2,5 it is difficult to choose whether to ascribe System 1 or System 2 based on time alone. It may be less important in the conclusion to assign causality to error and fall victim to the narrative fallacy2,5 (the tendency to perceive, or impose, causality based on the plausible) than it is to contribute to a robust model of how clinical reasoning may “really” work. As Groves6 suggests, (1) clinical reasoning is complex and involves a number of interacting elements, (2) it involves a dynamic interaction of content knowledge and critical thinking, (3) analytic and nonanalytic processes work in tandem, and (4) a definitive model of clinical reasoning includes the overarching role of metacognition. In that broader framing, perhaps error can occur at any time or place in the process, so System 1 and System 2 reasoning can be both functional and/or dysfunctional depending on the context. The above considerations make clear that it is easy to be too quick when arriving at conclusions about response times and diagnostic accuracy. David Petrie, MD, FRCP Professor and head, Department of Emergency Medicine, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; [email protected] Sam Campbell, MD Professor of emergency medicine, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, 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)0.013
metaresearch head score (Gemma)0.129
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: Commentary
Teacher disagreement score0.018
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.129
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0090.010
Open science0.0060.002
Research integrity0.0180.034
Insufficient payload (model declined to judge)0.0080.003

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 head0.055
GPT teacher head0.423
Teacher spread0.368 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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