Analysis of medical malpractice claims to improve quality of care: Cautionary remarks
Bibliographic record
Abstract
Medical malpractice claims can be analysed to gain insights aimed at improving quality of care. However, using medical malpractice claims in medical research raises epistemological and methodological concerns related to certain features of the litigation process. Medical research should therefore approach medical malpractice claims with caution. Taking one recent study as a an example, this article insists on three areas of concern: (a) the quantity of legal materials available for analysis; (b) the content of the legal materials available for analysis; and (c) the ways in which the content of the legal materials should be analysed and the types of inferences that it can support. The article concludes with general recommendations for future medical research that would incorporate medical malpractice claims. These recommendations centre around recognizing the qualitative dimension of legal reasoning.
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 | 0.322 | 0.504 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".