Bibliographic record
Abstract
OBJECTIVE: To compare maternal and neonatal outcomes in spontaneous versus induced labor after one previous cesarean delivery. METHODS: Women with one previous cesarean delivery who had spontaneous labor between January 1992 and January 2000 were compared with those whose labor was induced. RESULTS: Three thousand seven hundred forty-six patients had a trial of labor (2943 spontaneous, 803 induced). Those induced had more frequent early postpartum hemorrhage (7.3% versus 5.0%; odds ratio [OR] 1.66; 95% confidence interval [CI] 1.18, 2.32), cesarean delivery (37.5% versus 24.2%; OR 1.84; 95% CI 1.51, 2.25), and neonatal intensive care unit (NICU) admission (13.3% versus 9.4%; OR 1.69; 95% CI 1.25, 2.29). There was a trend toward higher uterine rupture rates in those with induced versus spontaneous labor (0.7% versus 0.3%, P =.128) and for patients undergoing dinoprostone (prostaglandin E(2)) induction versus other methods (1.1% versus 0.6%, P =.62), although neither difference achieved statistical significance. CONCLUSION: Induced labor is associated with an increased rate of early postpartum hemorrhage, cesarean delivery, and neonatal ICU admission. The higher rate of uterine rupture in those who had labor induced was not statistically significant.
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 | 0.001 | 0.008 |
| 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) | 0.002 | 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".