Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.033 | 0.083 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".