One‐year evaluation of the impact of an emergency obstetric and neonatal care training program in Western Kenya
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of introducing an emergency obstetric and neonatal care training program on maternal and perinatal morbidity and mortality at Moi Teaching and Referral Hospital, Eldoret, Kenya. METHODS: A prospective chart review was conducted of all deliveries during the 3-month period (November 2009 to January 2010) before the introduction of the Advances in Labor and Risk Management International Program (AIP), and in the 3-month period (August-November 2011) 1 year after the introduction of the AIP. All women who were admitted and delivered after 28 weeks of pregnancy were included. The primary outcome was the direct obstetric case fatality rate. RESULTS: A total of 1741 deliveries occurred during the baseline period and 1812 in the postintervention period. Only one mother died in each period. However, postpartum hemorrhage rates decreased, affecting 59 (3.5%) of 1669 patients before implementation and 40 (2.3%) of 1751 afterwards (P=0.029). The number of patients who received oxytocin increased from 829 (47.6%) to 1669 (92.1%; P<0.001). Additionally, the number of neonates with 5-minute Apgar scores of less than 5 reduced from 133 (7.7%) of 1717 to 95 (5.4%) of 1745 (P=0.006). CONCLUSION: The introduction of the AIP improved maternal outcomes. There were significant differences related to use of oxytocin and postpartum hemorrhage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".