Inpatient Versus Outpatient Induction of Labor in Low-Risk Pregnancies: A Retrospective Cohort Study [ID: 1377456]
Bibliographic record
Abstract
INTRODUCTION: To determine whether outpatient induction of labor (OP-IOL) using balloon catheters improved outcomes and resource utilization when compared with inpatient (IP)-IOL. METHODS: Outcomes in low-risk singleton pregnancies from April 2016 to December 2018 after introduction of an OP-IOL protocol were compared with those that underwent IP-IOL between April and December 2016 at the same institution. RESULTS: We included 225 IP- and 850 OP-IOL cases. Prostaglandin was more commonly employed in operator-determined IP-IOL (78.7%), while balloon catheters were almost exclusively used in OP-IOL (95.4%), consequent to a unit policy. There was no difference in caesarean deliveries (CDs) (adjusted odds ratio [aOR], 1.25 [95% CI, 0.76, 2.08]), neonatal intensive care unit admission, 5-minute Apgar scores less than 7, or maternal adverse events after onset of labor. Those undergoing OP-IOL were less likely to need a second induction agent (aOR 0.19 [95% CI 0.11, 0.33]) but experienced more maternal adverse events related to the induction (aOR 3.72 [95% CI 1.24, 11.75]), a longer median IOL-to-delivery interval (19.13 versus 30.14 hours [7.28, 11.80]), although a median of 13.83 of these 30.14 hours were spent outside the hospital. There were no differences in the admission-to-delivery (−1.45 hours [–3.29, 0.38]) and total hospitalization time (0.66 hours [–3.93, 5.25]). CONCLUSION: Although IP- and OP-IOL had comparable CD and neonatal outcomes, operator-determined IP-IOL had significantly shorter IOL-to-delivery. A universal policy of OP balloon catheters did not shorten duration of hospitalization, but increased IOL-to-delivery and maternal adverse events. Whether an individualized approach to OP-IOL could reduce resource utilization needs to be explored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".