Monitoring perinatal outcomes in hospitals in Kabul, Afghanistan: The first step of a quality assurance process
Bibliographic record
Abstract
OBJECTIVE: Afghanistan is one of the countries with highest maternal and perinatal mortality in the world. Lack of reliable data, however, makes it difficult to select and prioritise the interventions that would be most cost effective. To gain some evidence, we review and analyse perinatal outcomes in facilities in Kabul and examine the role of patient risk and clinical practice factors. METHODS: We used data for 2006 from a facility-based maternal and newborn surveillance system based on labour and delivery logbooks in the four government hospitals with maternity services in Kabul to analyse perinatal mortality and understanding potentially modifiable factors. RESULTS: Data was collected for 53,524 births during 2006. Perinatal mortality was 43.5 per 1000 total births and the stillbirth rate was 38. For babies with a birthweight of > or =2500 g, the risk of perinatal death if delivered by cesarean section was 3.57 (CI = 3.08-4.13) times the risk of those delivered vaginally. Babies born of mothers with risk factors were 6.49 (CI = 5.64-7.48) times more likely to die. The perinatal mortality rate in babies of women with risk factors undergoing cesarean section was 220.5 per 1000 total births. CONCLUSIONS: Facility-based monitoring of perinatal health is possible in resource-limited settings. The situation in hospitals in Kabul is precarious with high levels of perinatal mortality. Improved intrapartum care, especially for women with risk factors, is needed to positively impact perinatal health.
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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 | 0.037 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".