The risk factor of postpartum hemorrhage after cesarean section for placenta previa: A retrospective study
Bibliographic record
Abstract
Abstract Background Placenta previa could induce postpartum hemorrhage (PPH). Even cases with less intraoperative hemorrhage during cesarean section had the potential risk to develop PPH. But, there were less report about the predictive factor of PPH associated with placenta previa. The aim of this study was to identify the predictive factor for PPH for women with placenta previa after cesarean section. Methods Women with placenta previa who underwent cesarean section at our institution between January 2003 and February 2015 were identified. Women that received any hemostatic procedure such as intrauterine balloon tamponade and gauze infiltration during cesarean section were excluded. All women were classified into two groups: Group A or with PPH, defined as over 500 ml of hemorrhage after cesarean section, and Group B or without PPH. A retrospective analysis to identify the predictive factor of PPH was conducted. Results Out of 128 women, 10 (7.8%) women were included in Group A and 118 (92.2%) women in Group B. There was no statistical significance in maternal history between both groups. The number of women suspected to have placental adhesion was higher in Group A than Group B (p=0.006). Furthermore, the amount of intraoperative hemorrhage in Group A was higher than that in Group B (p=0.025). As treatment for PPH, more women in Group A received allogenic blood transfusion (p=0.003), and uterine artery embolization (p = 0.010). In univariate analysis, placental adhesion suspected by surgeon during cesarean section was the predictive factor for PPH with placenta previa (p=0.046). Conclusion When placental adhesion is suspected by surgeons during cesarean section, additional hemostatic procedure should be performed for possible PPH.
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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 | 0.001 |
| 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) | 0.001 | 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".