Do Plastic Surgery Residents Get Sued? An Analysis of Malpractice Lawsuits
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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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".