Investigating factors associated with childhood anemia in the first 6 months of life in rural Bangladesh
Bibliographic record
Abstract
Background: Anemia remains the most common disease in the world. Iron deficiency (ID), the main cause of anemia, affects over one‐third of the world's population. Infants aged 6 to 24 months are particularly impacted because of their rapid growth and increased iron needs due to expansion of their blood volume. However, some of these infants are already anemic by six months of age. Objective: To determine the prevalence of and investigate factors contributing to anemia at 6 months of age in Kaligonj, a rural sub‐district in Bangladesh. Methods: This was a prospective cohort study on a convenience sample of 424 infants born to iron‐supplemented mothers. The infants were followed from birth to six months. Birth weight was measured, along with weight and hemoglobin at six months. Data on feeding and care‐giving practices, health status, birth practices and parity were obtained via a questionnaire administered to the care‐giver at six months of age. Results: The study is currently underway; data from 23 infants are available. Eighteen out of the 23 infants (78.3%) were anemic (hemoglobin ≤ 110 g/L). The average hemoglobin was 98.5 g/L (SD: 14.7, range: 67–120). The average duration of exclusive breastfeeding was surprisingly low at 2 months and 10 days (SD: 58.3). Implications: Findings from this study will hopefully lead to a better understanding of factors contributing to IDA at six months of age. (H.J. Heinz Company & CIHR supported).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".