Lawsuits against plastic surgeons: Does locale affect incidence of claims?
Bibliographic record
Abstract
BACKGROUND: Physicians continue to practice in a very litigious environment. Some physicians try to mitigate their exposure to lawsuits by avoiding geographical locations known for their high incidence of medical malpractice claims. Not only are certain areas of the United States known to have a higher incidence of litigation, but it is also assumed that certain areas of the hospital incur a greater liability. There seems to be a medicolegal dogma suggesting a higher percentage of malpractice claims coming from patients seen in the emergency room (ER), as well as higher settlements for ER claims. OBJECTIVE: To determine if there is any validity to the dogma that a higher percentage of malpractice claims arise from the ER. METHODS: An analysis of common plastic surgery consults that result in malpractice claims was performed. The location where the basis for the lawsuit arose - the ER, office (clinic) or the operating room (OR) - was evaluated. The value of the indemnity paid and whether its value increased or decreased based on the location of the misadventure was evaluated. RESULTS: According to the data, which represented 60% of American physicians, there was a larger absolute number of malpractice claims arising from the OR, not the ER. However, the highest average indemnity was paid for cases involving amputations when the misadventure originated in the ER. CONCLUSIONS: The dogma that a greater percentage of lawsuits come from incidents arising in the ER is not supported. However, depending on the patient's injury and diagnosis, a lawsuit from the ER can be more costly than one from the OR.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".