Adverse Events in the Operating Room: Definitions, Prevalence, and Characteristics. A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Adverse events occur commonly in the operating room (OR) and often contribute to morbidity, mortality, and increased healthcare spending. Validated frameworks to measure and report postoperative outcomes have long existed to facilitate exchanges of structured information pertaining to postoperative complication rates in order to improve patient safety. However, systematic evidence regarding measurement and reporting of intraoperative adverse events (iAE) is still lacking. METHODS: We searched Ovid Medline, Embase, and Cochrane databases for articles published up to June 2016 that measured and reported iAE. We presented the terms and definitions used to describe iAE. We identified the types of reported iAE and summarized them into discrete categories. We reported frequencies of iAE by detection methods. RESULTS: Of the 47 included studies, 30 were cross-sectional, 14 were case-series, and 3 were cohort studies. The studies used 16 different terms and 22 unique definitions to describe 74 types of iAE. Frequencies of iAE appeared to vary depending on the detection methods, with higher numbers reported when direct observation in the OR was used to detect iAE. Twenty studies assessed severity of iAE, which were mostly based on whether they resulted in postoperative outcomes. CONCLUSIONS: This study systematically reviewed the current evidence on prevalence and characteristics of iAE that were detected by direct observation, reviews of patient charts, administrative data and incident reports, and surveys and interviews of healthcare providers. Our findings suggest that direct observation method has the most potential to identify and characterize iAE in detail.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".