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Record W2102358784 · doi:10.1017/s1368980009005886

Unexpectedly high early prevalence of anaemia in 6-month-old breast-fed infants in rural Bangladesh

2009· article· en· W2102358784 on OpenAlexafffund
Yaseer A. Shakur, Nuzhat Choudhury, SM Ziauddin Hyder, Stanley Zlotkin

Bibliographic record

VenuePublic Health Nutrition · 2009
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick ChildrenSick Kids Foundation
KeywordsMedicineAnthropometryBirth weightPediatricsPregnancyGestational ageAnemiaCohortDemographyLow birth weightObstetricsBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.280
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2009
Admission routes2
Has abstractyes

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