Maternal near miss and mortality due to postpartum infection: a cross-sectional analysis from Rwanda
Bibliographic record
Abstract
BACKGROUND: The objective of this study is to evaluate 'near miss' and mortality in women with postpartum infections. METHODS: We performed a retrospective review of all patients referred to the University Teaching Hospital of Kigali (CHUK) between January 2012 and December 2013. We identified 117 patients with postpartum infections. Demographic data, length of admission, location of referral, initial surgery and subsequent treatment modalities including antibiotic administration and secondary surgery were recorded. The primary outcome of interest was a composite of maternal mortality and "near miss" defined as more than one laparotomy with/without hysterectomy and prolonged hospitalization. RESULTS: Diagnoses at CHUK were: pelvic peritonitis (56 %), deep surgical site infection including fasciitis (17 %), and endometritis (15 %). The primary procedures performed prior to transfer were: cesarean section (81 %), septic abortion management (12 %), and vaginal delivery (7 %). Antibiotics were initiated prior to transfer in 66 % of women. Surgery was required in 73 % of patients. Hysterectomies were performed in 22 % of patients. Maternal death occurred in 5 % of the patient population. The primary outcome of severe maternal morbidity and mortality occurred in 90 patients (77 %). CONCLUSION: Peritonitis-primarily as a result of cesarean deliveries-is associated with significant morbidity and mortality in our population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".