Bibliographic record
Abstract
To the Editor: The Perspective by Norman and colleagues1 is at odds with the literature on rationality in decision making. Efforts to devalue cognitive bias by claiming the effects have only been demonstrated in undergraduates should be put to rest. Leading cognitive scientists describe such claims as “nonsense,”2 and Kahneman himself dismissed the notion as “cartoonish.”3 Widespread deviations from normative decision making, experimentally demonstrated over the last 40 years, have been replicated many times over in representative populations.2 Cognitive biases are real, abundant, and a major problem in clinical decision making. To deny their existence is irrational. Early in the article a straw man is erected by attributing clinical reasoning errors to either a lack of medical knowledge or to cognitive bias. Several studies have found cognitive failings are more likely due to how physicians think rather than what they know, and most clinicians confirm this is what happens in practice. Throughout the article, findings from a variety of studies are interpreted in a biased fashion. Demonstrations of the impact of cognitive bias on medical decision making are minimized or ignored. Major reviews are omitted, and there is a puzzling acquiescence by coauthors to discount their own published demonstrations of cognitive biases. The potential of cognitive bias mitigation (CBM) strategies is also minimized, and erroneous statements are made. An article of mine is cited to support their statement that “evidence is consistent in demonstrating that such strategies have no or limited effectiveness.”1 In fact, I was saying the opposite. In contrast to the view presented in this Perspective,1 a recent systematic review of 28 studies in CBM and dual process thinking led the authors to conclude, “Results to date are promising and this relatively young field is now close to a point where these kinds of cognitive interventions can be recommended to educators.”4 To delay this important initiative to reduce diagnostic failure would be unconscionable and unethical. Pat Croskerry, MD, PhDProfessor, Department of Emergency Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; [email protected]
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 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 | 0.012 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".