Anesthesiology airway-related medicolegal cases from the Canadian Medical Protection Association
Bibliographic record
Abstract
PURPOSE: We analyzed closed civil legal cases in 2007-2016 from the Canadian Medical Protective Association (CMPA) involving specialist anesthesiologists where airway management was the central concern. METHODS: We included all airway-related civil legal cases involving specialist anesthesiologists that closed from 2007 to 2016. The following variables were abstracted by CMPA medical analysts: clinical context, peer expert opinions of contributing factors, and patient and legal outcomes. RESULTS: We found 46 of the 406 (11%) closed cases involving anesthesiologists to be airway-related. Twenty-six cases (57%) involved elective surgery and 31 patients (67%) were categorized as American Society of Anesthesiologists physical status III. Twenty-five cases (54%) occurred outside the operating room (e.g., postanesthesia care unit, intensive care unit, or other satellite locations). In 19 (42%) cases, there was at least one predictor of a difficult airway. Peer experts identified judgement failures in 30 cases (65%), most commonly inadequate airway evaluation. In 30 cases (65%), the patient died or had a permanent brain injury. The medicolegal outcome favoured the patient in 27 (59%) cases, with a median [interquartile range] payment of 422,845 [257,637-935,673] CAD. CONCLUSIONS: Severe patient harm is common when airway management is the focus of a CMPA medicolegal complaint involving anesthesiologists. Patients were otherwise typically low risk cases presenting for elective surgery. Failure to assess or to change management based on the airway exam or encountered difficulty were the most common errors. Our findings support the continued need for adoption, adherence, and practice of guidelines for anticipated and unanticipated difficult airway management for every patient encounter.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".