Dissecting Medical Litigation: An Analysis of Canadian Legal Cases in Plastic Surgery
Bibliographic record
Abstract
Introduction: Plastic surgeons are more likely to face medical litigation, compared to other specialists. Although this has been previously studied in other countries, there is a paucity of data regarding legal medical cases within Canada. The goal of this study was to compile and analyze all medical litigations in plastic surgery in Canada and identify themes associated them. Methods: A systematic search of the 2 largest Canadian online legal databases, LexisNexis Canada and WestLawNext Canada, was conducted to retrieve all legal medical cases against plastic surgeons in Canadian courts. Quantitative and qualitative analyses were performed to dissect the characteristics of plastic surgery litigation in Canada. Results: A total of 105 legal cases were included in this analysis, including 81 lawsuits and 24 appeals. The preponderance of cases was related to breast surgeries (47.0%), followed by head and neck surgeries (18.1%), with 76.5% being related to cosmetic surgery; 64.2% were ruled in favour of the surgeon. The lack of preoperative informed consent was highly associated with a final ruling in favour of the patient ( P < .0001). The average monetary value of damages awarded was $61 076. There was no significant difference in monetary value between cosmetic and reconstructive cases. Conclusion: The majority of medical litigation in plastic surgery in Canada is associated with cosmetic surgeries, most commonly of the breast. Lack of informed consent is associated with judicial rulings in favour of patients. By understanding the themes underlying these legal cases, we hope to highlight the main issues that lead to litigation in plastic surgery.
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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 | 0.003 | 0.433 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.013 | 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".