Risk factors for postpartum hemorrhage following cesarean delivery
Bibliographic record
Abstract
Objective To identify risk factors for postpartum hemorrhage (PPH) following cesarean delivery (CD).Methods A retrospective study of all women who underwent CD in a university-affiliated tertiary hospital (2014–15). PPH was defined as any of the following: clinical PPH (≥1000 ml estimated blood loss), hemoglobin (Hb) drop ≥3 g/dl (the difference between pre-CD Hb level within a 24 h prior to the delivery) and post-CD (nadir level during the first 72 h after CD)) or the need for blood products transfusion. The characteristics of women with PPH following CD were compared to a control group of those with CD without PPH.Results Of the 15,564 deliveries during the study period, 3208 (20.6%) women met inclusion criteria, of them, 307 (9.6%) had PPH and 2901 (90.4%) served as controls. Women in the PPH group were younger (32.6 ± 5.3 vs. 33.5 ± 5.4, p = .006) and more often nulliparous (45.9% vs. 33.3%, p<.001) compared to the controls. However, there were no differences between the groups regarding the rate of multiple gestations, maternal diabetes mellitus, hypertensive disorders, polyhydramnios, and macrosomia. The rates of induction of labor (16.3% vs. 8.6%, p<.001) and urgent CD (47.9% vs. 32.0%, p<.001) were higher in the PPH group compared to the controls. In multivariate logistic regression, predictors for PPH following CD were (odds ratio, 95% confidence interval) urgent CS (1.57, 1.78–2.11, p = .002), CD duration (1.02, 1.01–1.03, p<.001), and the number of previous CDs (0.74, 0.62–0.90, p = .003).Conclusions In women undergoing cesarean section, urgent CD, the duration of the surgery, and the number of the previous CD are associated with the risk of PPH and should be taken into consideration during the postpartum assessment.
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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 | 0.002 |
| 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".