Transperineal ultrasonography angle of progression measurement as a predictor of successful labor in pregnancy with induction of labor: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Induction of labor (IOL) is a common obstretic procedure that carries maternal and fetal risks. The sonographic angle of progression (AOP), which is the angle between the pubic symphysis and the inferior portion of the fetal skull, has been proposed as a predictor of successful vaginal delivery (VD) in IOL, but its diagnostic value remains unclear.Objective To evaluate the diagnostic performance of AOP measured via transperineal ultrasonography (TPUS) in predicting successful IOL.Methods A systematic search was conducted in EMBASE, MEDLINE, Scopus, and Google Scholar until October 2024, following PRISMA guidelines. Eligible cohort studies reported AOP values and IOL outcomes, with successful IOL defined as vaginal delviery (VD). Data were analyzed using a random-effects model, with heterogeneity assessed using I2 statistics and publication bias evaluated with funnel plots and Egger’s test.Results Eight studies including 1,883 women were analyzed. Women with successful VD had a wider AOP than those undergoing cesarean delivery (CD) by 7.48 degrees (95% CI: 0.27–14.69; p = 0.04) with high heterogeneity (I2 = 97.59%). Sensitivity analysis reduced the mean difference to 4.64 degrees (95% CI: 2.61–6.68; p < 0.01), with lower heterogeneity (I2 = 49.78%). The pooled odds ratio (OR) showed no significant association (OR: 0.96, 95% CI: 0.93–1.00; p = 0.04) with high heterogeneity (I2 = 87.23%). Risk of bias was low based on the Newcastle-Ottawa scale, and publication bias analysis suggested asymmetry due to both bias and heterogeneity.Conclusion Angle of progression was wider in IOL with successful VD; however, no significant association was found.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".