Neonatal faecal abundance of <i>Bifidobacterium longum</i> subspecies <i>infantis</i> is not associated with anthropometric outcomes up to 6 months of age in Bangladeshi infants
Bibliographic record
Abstract
Abstract B. infantis abundance in the infant gut may be associated with growth and health outcomes. However, these relationships have not been widely studied in settings where B. infantis is a dominant early-life commensal and growth faltering is prevalent. Here, we estimated associations between neonatal B. infantis abundance and anthropometric outcomes up to 6 months of age in generally healthy infants in Dhaka, Bangladesh; diarrhoea and hospitalizations (at 1–2 and 6 months) were secondary morbidity outcomes. B. infantis stool absolute abundance was quantified by qPCR; for each infant, the primary exposure was mean abundance (0–28 days). Length-for-age, weight-for-age, and weight-for-length z-scores were derived at birth, 2, 3, and 6 months. Neonatal B. infantis abundance had a bimodal distribution, with 63% of infants having detectable B. infantis by 28 days of age. Anthropometric z-score distributions were shifted down, with means below zero. Neonatal B. infantis abundance was not associated with any anthropometric outcome at 2, 3, or 6 months of age ( n = 830 ), or with the risks of diarrhoea or hospitalizations. The lack of association of neonatal B. infantis abundance with growth outcomes suggests that promoting early B. infantis colonization is unlikely to improve growth in populations with postnatal faltering.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".