Patterns and trends among physicians-in-training named in civil legal cases: a retrospective analysis of Canadian Medical Protective Association data from 1993 to 2017
Bibliographic record
Abstract
<h3>Background:</h3> Medico-legal data show opportunities to improve safe medical care; little is published on the experience of physicians-in-training with medical malpractice. The purpose of this study was to examine closed civil legal cases involving physicians-in-training over time and provide novel insights on case and physicians characteristics. <h3>Methods:</h3> We conducted a retrospective descriptive study of closed civil legal cases at the Canadian Medical Protective Association (CMPA), a mutual medico-legal defence organization for more than 105 000 physicians, representing an estimated 95% of physicians in Canada. Eligible cases involved at least 1 physician-in-training and were closed between 1993 and 2017 (for time trends) or 2008 and 2017 (for descriptive analyses). We analyzed case rates over time using Poisson regression and the annualized change rate. Descriptive analyses addressed case duration, medico-legal outcome and patient harm. We explored physician specialties and practice characteristics in a subset of cases. <h3>Results:</h3> Over a 25-year period (1993–2017), 4921 physicians-in-training were named in 2951 closed civil legal cases, and case rates decreased significantly (β = −0.04, 95% confidence interval −0.05 to −0.03, where β was the 1-year difference in log case rates). The annualized change rate was −1.1% per year. Between 2008 and 2017, 1901 (4.1%) of 45 967 physicians-in-training were named in 1107 civil legal cases. Cases with physicians-in-training generally involved more severe patient harm than cases without physicians-in-training. In a subgroup with available information (<i>n</i> = 951), surgical specialties were named most often (<i>n</i> = 531, 55.8%). <h3>Interpretation:</h3> The rate of civil legal cases involving physicians-in-training has diminished over time, but more recent cases featured severe patient harm and death. Efforts to promote patient safety may enhance medical care and reduce the frequency and severity of malpractice issues for physicians-in-training.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".