Risk of postpartum hemorrhage with increasing first stage labor duration
Bibliographic record
Abstract
With increasing rates of postpartum hemorrhage (PPH) in high-income countries, an important clinical concern is the impact of labor duration on the risk of PPH. This study examined the relationship between increasing active first stage labor duration and PPH and explored the role of second stage labor duration and cesarean delivery (CD) in this association. Including 77,690 nulliparous women with spontaneous labor onset, first stage labor duration was defined as the time from 5 cm to 10 cm, second stage duration from 10 cm dilation to birth and PPH as estimated blood loss > 1000 ml. Using modified Poisson regression for risk ratios (RR) and confidence intervals (CI), we found a 1.5-fold (RR, 1.53; 95% CI, 1.41‒1.66) increased PPH risk when first stage of labor exceeded 12.1 h compared to the reference (< 7.7 h). Mediation analysis showed that 18.5% (95% CI, 9.7‒29.6) of the increased PPH risk with a prolonged first stage (≥ 7.7 h) was due to a prolonged second stage (> 3 h) or CD. These results suggest that including first stage duration in intrapartum assessments could improve PPH risk identification in first-time mothers with a singleton fetus in vertex presentation at full term with spontaneous labor onset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".