How Much Is Too Much? Intrapartum Interventions in a Term Singleton US Birth Cohort [19N]
Bibliographic record
Abstract
INTRODUCTION: Ideal rates of intrapartum interventions are unknown. Contemporary data from the US on the prevalence of these interventions is limited. The aim of this study was to evaluate rates of intervention in term singleton pregnancies. METHODS: This study uses a retrospective cohort of births at 17 hospitals in the Northwest US between 04/01/2017 and 03/31/2019. We report mode of delivery (cesarean with no labor/cesarean following labor/vaginal) and labor onset (spontaneous/induced). For spontaneous labors, we report rates of artificial rupture of the membranes (in those with intact membranes at admission), oxytocin augmentation, operative delivery, anesthesia (regional or general) and episiotomy. The proportion of pregnancies subject to none of these interventions was also evaluated. RESULTS: Of 47,770 singleton term pregnancies in this cohort, 8,485 (17.8%) had a cesarean without labor and 13,857 (29.0%) underwent induction of labor. Spontaneous labor occurred in 25,428 (53.2%) of all singleton term births. Interventions in spontaneous labor included: artificial rupture of the membranes in 63.9% (10,509/16,454) with intact membranes at admission, oxytocin augmentation in 9,227 (36.3%), cesarean birth in 2,594 (10.2%), operative vaginal birth in 1,250 (5.8%), anesthesia in 16,458 (64.7%) and episiotomy in 594 (2.3%). Only 4.6% (2,229) of all singleton term births followed spontaneous labor without any of the interventions evaluated. CONCLUSION: Intervention is common in this population and only 1 in 20 singleton term births occurred without any of the evaluated interventions. More research is needed into the effectiveness of obstetric interventions, particularly in spontaneous term 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".