Stillbirth in rural Bangladesh: arsenic exposure and other etiological factors: a report from Gonoshasthaya Kendra
Bibliographic record
Abstract
OBJECTIVE: To use data collected by Gonoshasthaya Kendra, a large nongovernmental organization providing health care to some 600 villages, to describe the epidemiological pattern of stillbirth and any additional contribution made by arsenic contamination of hand-pump wells in Bangladesh. METHODS: Completed pregnancies and outcomes (n = 30 984) for two calendar years, together with existing data on 26 socioeconomic and health factors were selected for study. The health care in these villages was administered from 16 geographical centres; information on the average arsenic concentration in each centre was obtained from the National Hydrochemical Survey. After univariate analysis, a multivariate, multilevel, logistic model for stillbirth was developed. The additional effect of arsenic was calculated having adjusted for all potential confounders thus identified. FINDINGS: The overall stillbirth rate was 3.4% (1056/30 984) and increased with estimated arsenic concentration (2.96% at < 10 microg/l; 3.79% at 10 microg/l to < 50 microg/l; 4.43% at > 50 microg/l). Having adjusted for 17 socioeconomic and health factors, the odds ratios estimated for arsenic (with < 10 microg/l as reference) remained raised: 1.23 (95% confidence interval, CI: 0.87A1.74) at 10 microg/l to < 50 microg/l and 1.80 (95% CI: 1.14A2.86) at 50 microg/l or greater. CONCLUSION: A increased risk of stillbirth is associated with arsenic contamination. This risk, substantial enough to be detected by an ecological approach and not readily attributable to unmeasured confounding, is essentially preventable and all efforts must be made to protect women at high risk.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".