Medical Malpractice Review Panels and Medical Liability System Cost, Timeliness, and Efficiency: A Cross‐Sectional Study
Bibliographic record
Abstract
The medical malpractice review panel is a widely adopted tort reform, but few empirical data exist regarding its utility. This cross‐sectional study evaluated potential associations between the medical malpractice panel status of a state (mandatory submission panel, optional submission panel, or no panel) and measures of cost, timeliness, and efficiency of medical malpractice claims resolution for the year 2002. Effects of differences in baseline state characteristics that could affect those associations were analyzed using a multiple regression analysis. After adjusting for significant covariates among measures of socioeconomic characteristics, judicial system frequency and function, and other statutory tort reforms, multiple regression analysis found no significant relationship at the state level in the year 2002 between the predictor variable, panel status, and the dependent variables of paid loss ratio, paid defense cost ratio, reported physician malpractice payment, dollars of paid defense cost per dollar of paid loss, reported annual physician malpractice insurance premium for internal medicine, general surgery, and obstetrics/gynecology, time from incident of malpractice to payment of claim, and ratio of paid to unpaid claims (p values > 0.05). These observations are consistent with the hypothesis that the medical malpractice review panel is a statutory reform of secondary or neutral effect.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".