MétaCan
Menu
Back to cohort
Record W2138081764 · doi:10.1097/acm.0000000000000566

In Reply to Webster

2015· letter· en· W2138081764 on OpenAlexaffabout
Pat Croskerry, David Petrie, James B. Reilly, Gordon Tait

Bibliographic record

VenueAcademic Medicine · 2015
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsVictoria General HospitalNova Scotia Health AuthorityCapital District Health Authority
Fundersnot available
KeywordsReflexivityAsideUnconscious mindProcess (computing)Point (geometry)Cognitive psychologyPsychologyComputer scienceReflection (computer programming)EpistemologyCognitive sciencePsychoanalysisSociology

Abstract

fetched live from OpenAlex

We agree with Webster’s call for more precise models of clinical decision making (CDM). The question is: What is meant by precision? High levels of analytic precision can readily be achieved in Type 2 processing, but overall precision, of the type seen in well-calibrated CDM, is what is needed. Importantly, the role played by Type 1 processing, which has received relatively little emphasis, needs full acknowledgment. Current dual process theory and neurophysiologic testing of the model have progressed considerably beyond earlier concepts of the conscious–unconscious mind. If the external validity of the two studies referred to by Webster is unknown, then it is difficult to see how any conclusions can be other than tenuous. Aside from the very nonclinical conditions under which Norman and colleagues’1 experiments were conducted, we questioned whether they had any bearing on Type 1 processing, as it remains unclear if the experimental designs involved other than Type 2 processing. To Webster’s next point, Type 1 processing is associative and often no more than autonomous reflexivity, so we should not refer to it as a “mode of thinking”; thinking implies a more deliberate process. Slowing down may not switch off Type 1 processes (although it can), but hopefully it may lead to reevaluation of the conclusions that result from them. Moreover, it seems that how people slow down is important. Mamede et al2 have demonstrated the improvements that result from structured reflection. Overall, the refinement of CDM skills comes from repeated practice—the intentional application of knowledge that leads to a decision, followed by reflection upon that process, and on its outcome. Structured feedback enriches the experience of reflection and the learning that results from it. It would be a practical impossibility for clinicians to evaluate every Type 1 decision—many are essential to well-calibrated CDM and most should be left alone; some, however, will need challenging. Further, we doubt that anyone seriously believes that exhorting doctors to try or think harder are solutions to diagnostic failures. Invariably, better-calibrated CDM is not a matter of effort but, rather, an understanding of how the process works, and the important equipoise of Type 1 and Type 2 processing. Pat Croskerry, MD, PhD Professor and director, Critical Thinking Program, Division of Medical Education, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; [email protected] David A. Petrie, MD Professor of emergency medicine and professor, Department of Emergency Medicine, Faculty of Medicine, Dalhousie University, and chief, Capital District Health Authority Department of Emergency Medicine, Halifax, Nova Scotia, Canada. James B. Reilly, MD, MS Director, Internal Medicine Residency, Allegheny General Hospital, Western Pennsylvania Hospital Educational Consortium, Pittsburgh, Pennsylvania, and assistant professor of medicine, Temple University School of Medicine, Philadelphia, Pennsylvania. Gordon Tait, PhD Assistant professor, Departments of Surgery and Anesthesia, and staff scientist, Department of Anesthesia, Toronto General Hospital, University Health Network, Faculty of Medicine, University of Toronto, 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)0.009
metaresearch head score (Gemma)0.082
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 score0.033
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.082
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0080.014
Open science0.0050.006
Research integrity0.0330.083
Insufficient payload (model declined to judge)0.0170.013

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 head0.077
GPT teacher head0.404
Teacher spread0.327 · 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
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAcademic MedicineSame topicClinical Reasoning and Diagnostic SkillsFrench-language works237,207