Impact of tariff refinement on the choice between scheduled C‐section and normal delivery: Evidence from France
Bibliographic record
Abstract
Studying quasi-experimental data from French hospitals from 2010 to 2013, we test the effects of a substantial diagnosis-related group (DRG) tariff refinement that occurred in 2012, designed to reduce financial risks of French maternity wards. To estimate the resulting DRG incentives with regard to the choice between scheduled C-sections and other modes of child delivery, we predict, based on pre-admission patient characteristics, the probability of each possible child delivery outcome and calculate expected differences in associated tariffs. Using patient-level administrative data, we find that introducing additional severity levels and clinical factors into the reimbursement algorithm had no significant effect on the probability of a scheduled C-section being performed. The results are robust to multiple formulations of DRG financial incentives. Our paper is the first study that focuses on the consequences of a DRG refinement in obstetrics and develops a probabilistic approach suitable for measuring the expected effects of DRG fee incentives in the presence of multiple tariff groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".