Association between Anesthesiologist Age and Litigation
Bibliographic record
Abstract
BACKGROUND: : The threat of being sued is a concern for many anesthesiologists. This paper asks whether litigation brought against anesthesiologists is associated with the age of the anesthesiologist. METHODS: : Institutional research ethics approval was granted. We obtained billing data for all procedures performed by specialist anesthesiologists stratified into three age groups (less than 51, 51-64, and 65 and older) from British Columbia, Quebec, and Ontario for the 10-yr period from Jan. 1, 1993 to Dec. 31, 2002. We also obtained all litigations (including disability weighted claims) handled by the Canadian Medical Protective Association during the same time period in which the Canadian Medical Protective Association experts considered the anesthesiologist cited to be at least partially responsible for the adverse event leading to the complaint. RESULTS: : In univariate analysis with the less than 51 age group as the reference category, the litigation rate ratio for the 51-64 age group was 1.14 (95% CI: 0.99-1.32) and for the 65 and older age group was 1.50 (95% CI: 1.14-1.97). Our analyses using disability weighted claims showed the 51-64 group to have 1.31 (95% CI: 0.95-1.80) and 65 and older group to have 1.94 (95% CI: 1.41-2.67) relative increase in disability compared to the less than 51 age group. CONCLUSIONS: : We found a higher frequency of litigation and a greater severity of injury in patients treated by anesthesiologists in the 65 and older group. The reasons for these findings should become an active field of research.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".