A Prediction Score for Maternal Mortality in Senegal and Mali
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a maternal mortality score to identify patients at risk of in-hospital death in developing countries. METHODS: We performed a prospective observational study in 46 referral hospitals in Senegal and Mali, starting October 1, 2007. Derivation of a maternal mortality score was performed, using generalized estimating equation, on patients included during the first 6 months of the study (301 deaths out of 43,624 deliveries) and validated on patients included during the next 6 months (345 deaths out of 46,328 deliveries). RESULTS: Nine criteria were independently associated with maternal death: severe anemia in pregnancy, malaria diagnosed during pregnancy, parity greater than 4, fewer than three antenatal visits, referral from another health facility, antepartum or postpartum hemorrhage, preeclampsia or eclampsia, uterine rupture, and genital infection or sepsis. The maternal mortality score, ranging from 0 to 100, occupies an area under the receiver operating characteristics curve of 0.89 (95% confidence interval [CI] 0.87-0.91). The low-risk group for maternal mortality, based on a score less than 10, has a negative predictive value of 99.9% (95% CI 99.8-99.9) and a negative likelihood ratio of 0.18, ruling out maternal mortality with a probability of 0.13% (95% CI 0.09-0.17). Sensitivity of the score to identify patients at risk of in-hospital death was 85.0% (95% CI 80.5-88.8). Validation of the score yielded a sensitivity of 87.8% (95% CI 83.9-91.1), a negative predictive value of 99.9% (95% CI 99.8-99.9), and a probability of maternal death of 0.12% (95% CI 0.08-0.17) in the low-risk group. CONCLUSION: : The maternal mortality score could help health care professionals to identify patients at risk of maternal mortality who need careful management. LEVEL OF EVIDENCE: III.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".