Pilot study of single‐use obstetric emergency medical kits to reduce maternal mortality
Bibliographic record
Abstract
OBJECTIVE: To describe the experience at a single facility regarding single-use emergency medication kits to treat obstetric emergencies in a resource-poor setting. METHODS: A retrospective study was conducted between October 2009 and October 2010 using data from the medical records of all patients treated with a single-use obstetric emergency medical kit (E-kit) during admission at the Riley Mother and Baby Hospital Wing, Eldoret, Kenya. Descriptive analyses were performed to quantify proportions of emergencies treated using E-kits in the first year of implementation. Summary statistics regarding maternal mortality from October 2008 to October 2010 were also retrieved to evaluate differences in the maternal mortality rates in the year of E-kit implementation and the year preceding implementation in order to estimate maternal mortalities averted with E-kit implementation. RESULTS: In the first year of implementation, 192 patients were treated using E-kits. Overall, 144 kits were used for treating postpartum hemorrhage, 52 for treating severe pre-eclampsia/eclampsia, and 1 for treating cardiopulmonary shock. There was a 30% reduction in maternal mortality ratio with E-kit implementation; however, results did not reach statistical significance. CONCLUSION: The results indicate that single-use E-kits may help to achieve a significant reduction in hospital rates of maternal 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".