Unexpectedly high early prevalence of anaemia in 6-month-old breast-fed infants in rural Bangladesh
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of anaemia and maternal and infant factors associated with Hb values in infants at 6 months of age in rural Bangladesh. DESIGN: Infants (born to mothers supplemented with Fe-folic acid from mid-pregnancy) were visited at birth and 6 months of age. Mothers' anthropometric status, and infants' birth weight, gestational age at birth, weight and Hb concentration at 6 months were measured. Household socio-economic and demographic data, infant feeding practices and health status were collected using a pre-tested structured questionnaire. SETTING: Rural Bangladesh. SUBJECTS: Four hundred and two infants. RESULTS: For the total cohort (n 402), the range of anaemia prevalence values was from 30.6 % using a cut-off value of Hb < 95 g/l to 71.9 % using a value of Hb < 110 g/l. Birth weight and month of birth were the only factors positively associated with infant Hb in a linear regression model (P = 0.008 and 0.011, respectively). CONCLUSIONS: There was an unexpectedly high prevalence of anaemia in infants at 6 months of age, before the assumed period of vulnerability. Hb at this age tended to be higher in those with higher birth weight. We also found a season effect on Hb, as it tended to be higher as the study progressed. The high prevalence of anaemia at such an early age needs to be addressed to minimize the disease's long-term consequences.
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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 | 0.000 | 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) | 0.001 | 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".