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Record W2330528987 · doi:10.1097/acm.0000000000000314

In Reply to Mamede and Schmidt

2014· letter· en· W2330528987 on OpenAlexaboutno aff
Jonathan S. Ilgen, Judith L. Bowen, Kevin W. Eva

Bibliographic record

VenueAcademic Medicine · 2014
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsnot available
Fundersnot available
KeywordsDeductive reasoningInductive reasoningEpistemologyReading (process)PsychologyLimitingCognitive psychologyRaising (metalworking)Computer scienceCognitive scienceArtificial intelligenceLinguisticsPhilosophyMathematics

Abstract

fetched live from OpenAlex

We appreciate Mamede and Schmidt’s thoughtful reading of our article. While they raise interesting hypotheses about methodological distinctions across studies, we do not see such differences as “conceptual and methodological shortcomings.” Instead, we think differences between their methodology and ours represent differing perspectives regarding the extent to which one can assume participants’ reasoning strategies based upon the experimental instructions they are given. The authors emphasize the importance of identifying contradictory features prior to generating diagnostic hypotheses; we are not convinced that such sequential representations of reasoning are realistic given the many decades psychologists required to generate separable measures of analytic and nonanalytic processes.1 In medicine, a series of studies have revealed that clinical features are more likely to be seen if one has the relevant diagnosis in mind,2,3 suggesting that reasoning is rarely solely inductive or deductive, and raising questions about the extent to which a clinician could ever be prevented from generating diagnoses when contemplating the features of a case. One of the definitional properties of nonanalytic reasoning is that it is fast, automatic, and largely beyond conscious control.4 This is not to say that the differences between Mamede and Schmidt’s methodology and ours could not account for the differences in results. Rather, it is meant simply to highlight one problem with labeling experimental conditions in these sorts of studies with theoretical constructs (i.e., “automatic reasoning”) rather than strictly limiting oneself to labels based on what was observably done (i.e., instructions to offer a diagnosis based on one’s first impression). The order of instructions may be important, but that is an empirical question that would need to be tested directly. Any such head-to-head comparison of processing interventions should, however, take into account a variety of methodologies suggesting that more analytic, conscious processing—in the absence of a preceding, experimentally induced bias—does not necessarily align with greater diagnostic accuracy.5,6 More generally, in testing the influence of such differences, it will be important to design interventions that are practically meaningful. We favor experimental control, but if the control is so great as to have little real-world value, then the benefit of any instructional intervention will be questionable. Rigid adherence to a reasoning protocol that requires strict researcher oversight is unlikely to be feasible for application in a naturalistic clinical setting. Jonathan S. Ilgen, MD, MCR Assistant professor, Division of Emergency Medicine, University of Washington, School of Medicine, Seattle, Washington; [email protected] Judith L. Bowen, MD Professor, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland, Oregon. Kevin W. Eva, PhD Professor and director of education research and scholarship, Department of Medicine, and senior scientist, Centre for Health Education Scholarship, University of British Columbia, Vancouver, British Columbia, 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)0.011
metaresearch head score (Gemma)0.091
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 score0.044
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0060.012
Open science0.0060.005
Research integrity0.0440.094
Insufficient payload (model declined to judge)0.0070.006

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 head0.045
GPT teacher head0.381
Teacher spread0.336 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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