The risk of cesarean delivery after labor induction among women with prior pregnancy complications: a subgroup analysis of the AFFIRM study
Bibliographic record
Abstract
BACKGROUND: To determine the risk of cesarean delivery after labor induction among patients with prior placenta-mediated pregnancy complications (pre-eclampsia, late pregnancy loss, placental abruption or intrauterine growth restriction). METHODS: The AFFIRM database includes patient level data from 9 randomized controlled trials that evaluated the role of LMWH versus no LMWH during pregnancy to prevent recurrent placenta-mediated pregnancy complications. The primary outcome of this sub-study was the proportion of women who had an unplanned cesarean delivery after induction of labor compared to after spontaneous labor. RESULTS: There were 512 patients from 7 randomized trials included in our sub-study. There was no difference in the risk of cesarean delivery between women with labor induction (21/148, 14.2%) and spontaneous labor (79/364, 21.7%) (odds ratio (OR) 0.60, 95% CI, 0.35-1.01; p = 0.052). Among 274 women who used LMWH prophylaxis during pregnancy, the risk of cesarean delivery was lower among those that underwent labor induction (9.8%) compared to spontaneous labor (22.4%) (OR 0.38, 95% CI, 0.17-0.84; p = 0.01). CONCLUSIONS: The risk of cesarean delivery is not increased after labor induction among a higher risk patient population with prior pregnancy complications. Our results suggest that women who receive LMWH during pregnancy might benefit from labor induction.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".