Damage Caps and Defensive Medicine: Reexamination with Patient‐Level Data
Bibliographic record
Abstract
Physicians often claim that they practice “defensive medicine,” including ordering extra imaging and laboratory tests, due to fear of malpractice liability. Caps on noneconomic damages are the principal proposed remedy. Do these caps in fact reduce testing, overall health‐care spending, or both? We study the effects of “third‐wave” damage caps, adopted in the 2000s, on specific areas that are expected to be sensitive to med mal risk: imaging rates, cardiac interventions, and lab and radiology spending, using patient‐level data, with extensive fixed effects and patient‐level covariates. We find heterogeneous effects. Rates for the principal imaging tests rise, as does Medicare Part B spending on laboratory and radiology tests. In contrast, cardiac intervention rates (left‐heart catheterization, stenting, and bypass surgery) do not rise (and likely fall). We find some evidence that overall Medicare Part B rises, but variable results for Part A spending. We find no evidence that caps affect mortality.
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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 | 0.064 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".