135. THE CANADIAN ORTHOPAEDIC MEDICOLEGAL CLIMATE: GLOBAL WARMING OR ISOLATED COOLING?
Bibliographic record
Abstract
Purpose: Litigation continues to be a concern in orthopaedic surgery despite suggestions on how to contain liability. The purpose of this study was to characterize orthopaedic litigation in Canada from 1997–2006. Method: This study reviewed all closed claims reported to the Canadian Medical Protective Association (CMPA) for 1997–2006 in which orthopaedic surgeons were named. There were 11,983 closed legal actions involving CMPA members (> 73,000 physicians), and 1,353 involved orthopaedic surgeons. A careful review of closed legal actions is a recognized tool for risk identification, assessment and management. The CMPA identifies any critical incidents within the closed legal files. A critical incident is defined as any omission or commission in the evaluation or management which led to the problem(s) that triggered the legal action. Each closed legal action can have more than one critical incident. Results: Performance, diagnostic and communication issues were the most frequently identified problems. These three areas account for 55% of the critical incidents identified. Performance related issues accounted for 395 critical incidents (29%). Diagnostic issues, including deficient histories and general evaluations, were identified in 281 cases (21%). Communication-related critical incidents included those concerning informed consent. The lack of informed consent was a common allegation, proven in 71 cases. In 439 cases (32%) there was no identifiable critical incident for the orthopaedic surgeon involved. Seventy-eight per cent of patients experienced minor or no disability and 22% experienced major disability or death. Events related to tibia trauma and knee arthroscopy formed the two major categories of claims. Patient care areas of high risk include the operating room and outpatient clinic. Overall, 31% of legal actions against orthopaedic surgeons had outcomes in favour of the plaintiffs, compared with 33% of all CMPA members’ claims. Conclusion: Although the likelihood for an orthopaedic surgeon to be sued in Canada has decreased over the last 10 years, the percentage of legal cases resolved in favour of plaintiffs has remained stable. Performance-related deficiencies, delays in diagnosis, and failures in communication represent areas of high medico-legal risk. Suggestions for risk management are provided to further decrease adverse events and the medico-legal risks for Canadian orthopaedic surgeons.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".