A scoping review protocol for cognitive bias in US and Canadian medical diagnosis: Current prevalence and management through education and policy
Bibliographic record
Abstract
In 2002, psychologist Daniel Kahneman’s received the Nobel Prize for his work on errors in judgement and decision-making. Kahneman’s and Amos Tversky’s collaboration, dating back to the 1990’s, with Canadian physician Donald Redelmeier, advanced work on cognitive biases in medical decision-making. In 2005, US and Canadian physicians began to publish regularly on cognitive bias in medical diagnosis. The literature through 2017 suggests missed or delayed diagnoses were implicated in 10-15% of cases, 17% of adverse events and 10% of deaths. Research also suggested cognitive bias was associated with 74% of missed or delayed diagnoses, regardless of whether a condition was rare or common or of the degree of confidence in the incorrect diagnosis. Given the well-documented importance of reducing cognitive bias in order to promote medical diagnostic accuracy, the current scoping review will integrate the English language reviews and literature 2005 through 2020 based on vignettes, case-studies, surveys, and medical record searches as well as policy and theory. It is anticipated the scoping review will document prevalent and overlooked biases; include theory and findings about circumstances that promote bias; and align policy recommendations with interventions that have more or less robust evidence for their efficacy. Given the history of the field, the review concentrates on medical diagnosticians and their patients in the USA and 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 | 0.173 | 0.225 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.086 | 0.017 |
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".