Bibliographic record
Abstract
OBJECTIVE: The improvement of obstetric services is one of the key components of the Safe Motherhood Programme. Reviewing maternal deaths and complications is one method that may make pregnancy safer, but there is no evidence about the effectiveness of this strategy. The objective of our before and after study is to assess the effect of facility-based maternal deaths reviews (MDR) on maternal mortality rates in a district hospital in Senegal that provides primary and referral maternity services. METHODS: We included all women who were admitted to the maternity unit for childbirth, or within 24 hours of delivery. We recorded maternal mortality during a 1-year baseline period from January to December 1997, and during a 3-year period from January 1998 to December 2000 after MDR had been implemented. Effects of MDR on organization of care were qualitatively evaluated. FINDINGS: The MDR strategy led to changes in organizational structure that improved life-saving interventions with a relatively large financial contribution from the community. Overall mortality significantly decreased from 0.83 (95% CI (confidence interval) = 0.60 -1.06) in baseline period to 0.41 (95% CI = 0.25 -0.56) per 100 women 3 years later. CONCLUSION: MDR had a marked effect on resources, management and maternal outcomes in this facility. However, given the design of our study and the local specific context, further research is needed to confirm the feasibility of MDR in other settings and to confirm the benefits of this approach for maternal health in developing countries.
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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 | 0.000 |
| 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 | 0.001 |
| 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".