Identifying females before childbirth to improve iron status.
Bibliographic record
Abstract
Background: It has recently been advocated that iron supplementation begin before childbearing. A key operational issue is how to identify females before pregnancy. Objective: To examine factors associated with time to first pregnancy and adolescent pregnancy. Methods: A cross‐sectional survey in Gazipur, Bangladesh from April to May 2006 among ever married females aged less than 50 years. Results: Data on 603 females were analyzed. Median age was 31 years (15–49). Median age at marriage was 17 years (11–31). Only 15% (7/47) of never pregnant females reported iron use in the past 6 months. 58% (322/556) of females were <19 years old at first pregnancy. Median time from marriage to first pregnancy was 12 months (0–408). Multivariate hazard analysis found risk of pregnancy increased by 13% for every one‐year increase in age at marriage (p<0.0001) and decreased by 3% for every one‐year increase in female¡ ¯ s current age (p<0.0001). Risk among medical contraceptive users was 58% of the risk of non‐users (p=0.0001).Using multivariate logistic regression analysis, probability of an adolescent pregnancy decreased by 3% for each year of marriage during adolescence (CI: 0.95–0.99, p=0.01), by 10% for each year increase in husband¡ ¯ s age at marriage (0.87–0.94, p<0.0001), by 68% among female wage earners compared to non‐wage earners (0.16–0.64, p=0.001) and by 50% among medical contraceptive users compared to non‐users (0.30–0.85, p=0.01). Conclusions: In this population females are married at a young age with short time to 1st pregnancy intervals. Periconceptional iron supplementation programs should target adolescents and newly‐weds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".