Cognitive biases in surgery: systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Although numerous studies have established cognitive biases as contributors to surgical adverse events, their prevalence and impact in surgery are unknown. This review aimed to describe types of cognitive bias in surgery, their impact on surgical performance and patient outcomes, their source, and the mitigation strategies used to reduce their effect. METHODS: A literature search was conducted on 9 April and 6 December 2021 using MEDLINE, Embase, PsycINFO, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews. Included studies investigated how cognitive biases affect surgery and the mitigation strategies used to combat their impact. The National Institutes of Health tools were used to assess study quality. Inductive thematic analysis was used to identify themes of cognitive bias impact on surgical performance. RESULTS: Thirty-nine studies were included, comprising 6514 surgeons and over 200 000 patients. Thirty-one types of cognitive bias were identified, with overconfidence, anchoring, and confirmation bias the most common. Cognitive biases differentially influenced six themes of surgical performance. For example, overconfidence bias associated with inaccurate perceptions of ability, whereas anchoring bias associated with inaccurate risk-benefit estimations and not considering alternative options. Anchoring and confirmation biases associated with actual patient harm, such as never events. No studies investigated cognitive bias source or mitigation strategies. CONCLUSION: Cognitive biases have a negative impact on surgical performance and patient outcomes across all points of surgical care. This review highlights the scarcity of research investigating the sources that give rise to cognitive biases in surgery and the mitigation strategies that target these factors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.723 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it