Cervical Length as a Predictor of Latency Interval in Twin Pregnancies With Preterm Prelabor Rupture of Membranes [35H]
Bibliographic record
Abstract
INTRODUCTION: Preterm prelabor rupture of membranes (PPROM) is a significant risk factor for preterm birth, carrying medical, financial and psychological burden. Predicting onset of labor after PPROM can result in timely interventions, including appropriate transfer to tertiary care centers. Ultrasound-measured cervical length can predict preterm delivery in singleton pregnancies with PPROM, but no data exists in twins. Therefore, we sought to determine whether cervical length could predict latency interval in twin pregnancies with PPROM. METHODS: Using the BORN Database, we performed a retrospective study identifying 43 twin pregnancies between 2012-2016 complicated by PPROM at McMaster University, Canada. Cervical length was determined by ultrasound measurement. We then compared our primary outcome, latency to labor in those pregnancies with cervical lengths less than 25 mm to those greater than 25 mm and results were analyzed by Mann-Whitney statistical analysis. Additional secondary measures compared the groups on length of stay, PPROM parameters and neonatal outcomes. RESULTS: We determined that the average latency interval in those twin pregnancies with cervical lengths less than 25 mm is statistically significantly shorter than in those with cervical lengths greater than 25 mm (49.2 vs. 196.0 hours, p=0.035). The average length of stay was also significantly greater in those with longer cervical lengths (5.52 vs. 11.05 days, p=0.03). Potential confounders such as parity, chorionicity, erythromycin or magnesium sulfate did not have any significant effect by ANCOVA regression analysis. CONCLUSION: In those twin pregnancies complicated by PPROM, cervical lengths less than 25 mm are associated with shorter latency intervals, which may prompt critical, timely intervention in this group.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".