Bibliographic record
Abstract
CONTEXT: There is a growing literature on diagnostic errors. The consensus of this literature is that most errors are cognitive and result from the application of one or more cognitive biases. Such biased reasoning is usually associated with 'System 1' (non-analytic, pattern recognition) thinking. METHODS: We review this literature and bring in evidence from two other fields: research on clinical reasoning, and research in psychology on 'dual-process' models of thinking. We then synthesise the evidence from these fields exploring possible causes of error and potential solutions. RESULTS: We identify that, in fact, there is very little evidence to associate diagnostic errors with System 1 (non-analytical) reasoning. By contrast, studies of dual processing show that experts are as likely to commit errors when they are attempting to be systematic and analytical. We then examine the effectiveness of various approaches to reducing errors. We point out that educational strategies aimed at explaining cognitive biases are unlikely to succeed because of limited transfer. Conversely, there is an accumulation of evidence that interventions directed at specifically encouraging both analytical and non-analytical reasoning have been shown to result in small, but consistent, improvements in accuracy. CONCLUSIONS: Diagnostic errors are not simply a consequence of cognitive biases or over-reliance on one kind of thinking. They result from multiple causes and are associated with both analytical and non-analytical reasoning. Limited evidence suggests that strategies directed at encouraging both kinds of reasoning will lead to limited gains in accuracy.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".