Secular trends in trial of labor and associated neonatal mortality and morbidity in the United States, 1995 to 2002.
Bibliographic record
Abstract
OBJECTIVE: A proportion of elective repeated cesarean sections where a trial of labor in a uterus with a previous scar was not attempted is on the increase. This study aimed to assess how reduced the use of trial of labor has impacted on neonatal outcomes in the United States. METHODS: Pregnant women with one previous cesarean delivery and a singleton live birth of the index pregnancy were abstracted from the 1995 to 2002 birth registration data of the United States. Adjusted odds ratios for adverse neonatal outcomes of trial of labor were estimated by multiple logistic regression models, in overall study subjects and in the two periods with high and low rates of trial of labor. RESULTS: A total of 1833407 eligible subjects were included in the analysis. Rate of trial of labor after one previous cesarean section dropped from 38.5% in 1995 to 15.0% in 2002. No significant change was observed in the patient population profile. Successful vaginal birth after cesarean delivery (VBAC) also declined from 76.6% in 1995 to 66.0% in 2002. A trial of labor after one previous cesarean section was correlated with increased risks of asphyxia-related neonatal death and neonatal morbidity. This risk was even more pronounced in low risk women and in the last study years with the lowest rate of trial of labor. CONCLUSION: The reduced use of trial of labor after one cesarean delivery in recent years in the United States has actually resulted in increased risk of adverse neonatal outcomes associated with a trial of labor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".