Preventable adverse events in surgical patients: A meta-analysis and knowledge, attitude, and practice (KAP) assessment
Bibliographic record
Abstract
All surgical procedures come with a risk of adverse events (AEs). To improve patient safety and prevent similar errors in the future, errors must be acknowledged and addressed. In this study a meta-analysis of patient safety literature in surgery was conducted and a Knowledge, Attitudes, and Practice (KAP) assessment survey of Calgary academic surgeons was performed.Results of the meta-analysis demonstrated a preventable adverse event (PAE) rate of 10.5 PAEs per 100 patients across all surgical specialties and a preventable death rate of 0.5 per 100 surgical patients. The KAP survey assessment demonstrated that 20% of surgeons could correctly identify the definition of both AE and error. Participants reported the factors contributing to an error to be multifactorial. The most frequently used methods to teach patient safety were Morbidity and Mortality rounds and individual feedback. Less than 25% of surgeons track their own AE rate. These results have implications for surgical postgraduate education, as well as for surgical practice in Canada. Recommendations are made for the development of a formal patient safety curriculum for all surgical trainees, with the aim of decreasing the number of errors. In addition, it is essential that more high-quality studies that include reproducible methods and consistent definitions of AEs and errors be conducted.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.051 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".