Reducing post‐cesarean sepsis: Current best practice in prevention and treatment
Bibliographic record
Abstract
Cesarean section is the most common surgical procedure performed worldwide. It is associated with good perinatal and maternal outcomes when indicated. The rising global cesarean birth rate has coincided with an increase in post-cesarean sepsis - specifically site infections, which have an incidence of 7% worldwide. Post-cesarean sepsis remains a serious complication that prolongs hospital stays, resulting in additional surgery and worsening maternal morbidity and mortality, and increasing healthcare costs with socioeconomic consequences. There is no global practice guide for post-cesarean sepsis, despite most maternal deaths due to sepsis occurring postpartum. Here we introduce a FIGO Committee on Infections During Pregnancy guide on prevention and treatment of post-cesarean sepsis. We encourage strategies to keep cesarean birth rates at evidence-based levels by aiming to standardize the management of labor and to increase the percentage of vaginal births after a previous cesarean. Identification of risk factors before and during the surgery is the primary step towards prevention. These measures, combined with evidence-based strategies to promote infection prevention practices, including routine prophylactic antibiotics, skin and vaginal preparation before cesarean birth, and glove change prior to skin closure, contribute towards reducing maternal morbidity and mortality.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".