Effect of Fear of Litigation on Obstetric Care: A Nationwide Analysis on Obstetric Practice
Bibliographic record
Abstract
The aim of our study was to investigate the influence of malpractice premiums paid by obstetricians on obstetric care across the United States. We conducted a retrospective cross-sectional population-based study using patient-level data obtained from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample on every woman who delivered in 2006. Mode of delivery was compared with the average state medical liability insurance premium paid by obstetricians (Medical Liability Monitor and the National Association of Insurance Commissioners) using a generalized estimating equation to calculate crude and adjusted odds ratios. Our cohort included 890,266 women who delivered across 37 states in 2006. Average state malpractice premium of over $100,000 was associated with higher incidences of total cesarean deliveries (odds ratio [OR] 1.17, 95% confidence interval [CI]: 1.02, 1.35); lower incidences of vaginal births after cesarean deliveries (OR 0.60, 95% CI: 0.37, 0.98); and lower rates of instrumental deliveries (OR 0.72, 95% CI: 0.63, 0.83) compared with when the average state malpractice premium was less than $50,000. Fear of litigation appears to have a marked effect on obstetric practice, particularly total cesarean delivery, vaginal birth after cesarean, and instrumental delivery, when malpractice premiums rise above $100,000 per annum.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".