Maternal morbidity in early pregnancy in rural northern Bangladesh
Bibliographic record
Abstract
OBJECTIVE: To determine the burden of maternal morbidity in early pregnancy in rural northern Bangladesh. METHODS: A cross-sectional analysis was performed on baseline morbidity data from 42 896 pregnant women enrolled in a vitamin A supplementation trial. One-week histories for 31 defined symptoms were collected at 5-12 weeks of gestation. Ten illnesses were defined, compatible with ICD-10 diagnoses and WHO definitions. Prevalence, duration, and treatment-seeking behaviors were determined for each symptom and illness. Risk of wasting malnutrition was compared between symptomatic and asymptomatic women. RESULTS: In total, 93.1% of women reported at least 1 symptom. The most frequent symptoms were poor appetite (53.3%), vaginal discharge (48.7%), and nausea (48.1%), each of which lasted 22-27 days. The most prevalent illnesses were anemia (36.4%), morning sickness (17.2%), excessive vomiting (7.0%), and reproductive tract infections (6.7%). Symptoms that prompted treatment seeking included jaundice, high-grade fever, and swelling of hands and face. Odds ratios for malnutrition were higher among women with symptoms of anemia (1.30; 95% confidence interval [CI], 1.24-1.36), vaginal discharge (1.37; 95% CI, 1.31-1.43), and high-grade fever (1.23; 95% CI, 1.10-1.37) than among those without symptoms. CONCLUSION: Women in rural Bangladesh report substantial morbidity in the first trimester.
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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 | 0.000 | 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 | 0.000 |
| 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".