The ARRIVE Effect: What Do the Real-World Data Show? [A251]
Bibliographic record
Abstract
INTRODUCTION: Published in August 2018, the ARRIVE trial found that induction of labor (IOL) of low-risk, nulliparous patients at a gestational age (GA) of 39 weeks decreased the risk of cesarean delivery (CD). We assessed the rate of elective IOL and CD rates post-ARRIVE in the U.S. METHODS: The 2018–2020 U.S. Vital Statistics birth certificate database was used. Nulliparous, singleton, low-risk pregnancies at 39 weeks of gestation or greater were included. Data was analyzed by county and quarter, including counties with more than 100 births in all quarters. Comparisons were made between April and June 2018, the quarter just prior to ARRIVE publication, and October and December 2020, the latest available quarter. RESULTS: A total of 297 counties were included, representing over 120,000 births per quarter. The rate of all IOL ≥39 weeks GA increased significantly, from 35% (±9%) to 41% (±11%) (paired t test, P<.001), as did the rate of IOL specifically in the 39th week, from 13% (±6%) to 18% (±8%) (paired t test, P<.001). An interrupted time series analysis showed that the rate of IOLs in the 39th week was rising pre-ARRIVE, but the rate of change was significantly greater post-ARRIVE (0.3% increase per quarter in 39-week IOL post-ARRIVE versus 0.19% increase pre-ARRIVE, P<.001). There was no change, however, in the CD rate: 25.1% (±5%) to 24.7% (±5%) (paired t test, P=.054). CONCLUSION: Since the publication of the ARRIVE trial, nulliparous, singleton, low-risk ≥39-week inductions have significantly increased across the U.S. at a greater rate than prior to ARRIVE. There has been no change in the rate of CD.
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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 | 0.045 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".