Think rationally rather than intuitively to avoid diagnostic errors, doctors are told
Bibliographic record
Abstract
Diagnostic errors—the most common reason for patients to sue doctors—occur partly because doctors depend too much on their intuition when making clinical decisions, a conference on safety heard last week. Pat Croskerry, professor in emergency medicine at Dalhousie University, Halifax, Nova Scotia, and a doctor of psychology, said that doctors were too quick to base their clinical decisions on intuition rather than on rational analysis. “Our intuition will always override analytical reasoning. We prefer to be in the intuitive mode; it is comfortably numb, but it gives you a misplaced feeling of security,” he said last week at a conference on patients’ safety hosted by Great Ormond Street Hospital for Children and supported by the BMJ . “The diagnostic failure rate approaches 15%. This is staggering. …
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".