Do Plastic Surgery Residents Get Sued? An Analysis of Malpractice Lawsuits
Why this work is in the frame
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Bibliographic record
Abstract
Trainees may be implicated in malpractice lawsuits. Our study examines malpractice cases involving plastic surgery trainees. Methods: Using the LexisNexis database, verdicts and settlements from appellate state and federal cases between February 1988 and 2020 were queried. A nonrepresentative sample of 300 cases was compiled. Results: During a 32-year period, 21 lawsuits involving plastic surgery trainees were identified. Of these, 14 (66.67%) involved claims when a trainee was directly named as a defendant. Eighteen (85.7%) cases were due to procedural-related adverse outcomes, while three (14.3%) cases were associated with clinical or diagnostic-related adverse outcomes. Of the procedure-related cases, five (27.8%) occurred when the trainee was the lead surgeon. Allegations included lack of informed consent of procedure complications (11, 52.4%), procedural error (11, 52.4%), failure to supervise trainee (11, 52.4%), inexperience of trainee (eight, 38.1%), incorrect diagnosis or treatment (five, 23.8%), delay in evaluation (three, 14.3%), lack of awareness of resident involvement (three, 14.3%), lack of follow-up (three, 14.3%), and prolonged operative time (one, 4.8%). Median time from injury to lawsuit resolution was 3.8 years [interquartile range (IQR), 3-5 years]. Verdicts were ruled in favor of the defense in eight (38.1%) cases and for plaintiff in six (28.6%) cases. A settlement was made in seven (33.3%) cases. Median payout for plaintiff-won cases was $5,100,000 (IQR, $1,530,000-$17,500,000); the median settlement was $2,500,000 (IQR, $262,500-$4,410,000). Conclusions: Procedural error, improper informed consent, improper trainee supervision, and resident inexperience were the most common allegations. These factors can lead to financial and psychological burdens early in a physician's career.
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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 | 0.007 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 it