The Medical Malpractice Explosion: An Empirical Assessment of Trends, Determinants, and Impacts
Bibliographic record
Abstract
This article briefly describes trends in the frequency and severity of medical malpractice claims in Canada and the U.S., with some comparative references to trends in Britain and Australia. In all cases, frequency and severity rates appear to have risen quite dramatically over the past decade and a half. The article proceeds to explore various hypotheses that might explain these trends. While empirical analysis does not yield firm conclusions, the fact that so many jurisdictions have experienced a somewhat similar phenomenon makes it doubtful that the primary cause of the increase is likely to be idiosyncratic features of one particular country's tort system. Instead, the authors conjecture that various changes in medical technology may well be a more important explanatory factor. The article goes on to examine the empirical evidence on the impact of expanding liability on physician behaviour and in turn whether observed changes in physician behaviour have caused reductions in the medical injury rate. While it seems clear from the evidence that the liability system has 'induced various changes in physician behaviour, it is much less clear whether these changes have reduced the medical injury rate or are otherwise socially desirable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".