Medico-legal Closed Case Trends in Canadian Plastic Surgery: A Retrospective Descriptive Study
Bibliographic record
Abstract
To enhance patient safety and prevent medico-legal complaints, we need to understand current trends and impacts. We aimed to characterize Canadian plastic surgery medico-legal patterns in many dimensions. METHOD: This retrospective descriptive analysis of Canadian Medical Protective Association data between January 1, 2013 and December 31, 2017 included closed regulatory body complaints and civil-legal actions involving plastic surgeons. We excluded class action legal cases and hospital complaints. We collected data on patient allegations, procedure types, healthcare-related patient harms, and peer expert criticisms. The primary outcome of interest was physician medico-legal outcome. RESULTS: We found 414 cases that met the inclusion criteria: 253 (61.1%) cases involved cosmetic procedures and 161 (38.9%) noncosmetic procedures. The annual incidence among plastic surgeon members of regulatory body complaints and civil-legal actions was 12.1% and 6.7%, for a combined incidence of 18.8%. The most common allegations were deficient clinical assessment, inadequate informed consent, delayed or misdiagnosis, and inadequate monitoring. Leading contributing factors were physician-patient communication breakdown, deficient clinical judgments, and inadequate documentation. The top procedural complications included cosmetic deformity, poor scarring, upper extremity stiffness or deficit, major structural injury, and mental health disorder. Less than half of cases (198/414, 47.8%) had unfavorable medico-legal outcomes for the surgeon. Patients were compensated in 86/198 (43.4%) of civil-legal cases. CONCLUSIONS: Plastic surgeons experience more medico-legal complaints for cosmetic versus noncosmetic procedures. To minimize medico-legal risks, plastic surgeons should focus on strong physician-patient communication, patient education/consent, thorough clinical assessment, minimizing potentially preventable complications, and maintaining relevant documentation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".