Do damages caps reduce medical malpractice insurance premiums? A systematic review of estimates and the methods used to produce them
Bibliographic record
Abstract
Despite common claims made in policy debates, the theoretical connection between tort reform and medical malpractice insurance premiums is ambiguous. Simple models suggest reforms such as statutory damages caps reduce premiums. More elaborate models that account for changes in physician behavior suggest caps might increase or have no impact on premiums. A number of empirical studies have been conducted to estimate the impacts of caps on premiums, and several qualitative literature reviews have attempted to draw general conclusions from the literature. No review, however, has offered a comprehensive and systematic analysis of the full set of empirical studies. This chapter fills that gap. We provide a first glimpse at the wide methodological variations in the studies that employ regression analysis to estimate the impacts of caps on medical malpractice insurance premiums. We describe 16 empirical studies that report 197 estimates of the impact of caps on premiums. Using a theory-driven framework to develop a set of best practices, we find that little weight can be put on any one study due to broad methodological shortcomings. This chapter highlights the need for better data and additional research on the impact of caps on premiums.
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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 | 0.020 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".