Relationships between infant morbidity, iron deficiency and growth
Bibliographic record
Abstract
Objective To examine how caregiver infant morbidity reports relate to biomarkers of inflammation, iron deficiency and infant growth. Methods Among 497 infants age 6–12 months, morbidity was collected by 15 day caregiver recall. Biomarkers for iron deficiency (ID; ferritin< 12) and inflammation (CRP: C ‐ reactive protein >; 10mg/L); and anthropometry (measured weight and length) were collected. Stunting (HAZ<−2), wasting (WHZ <−2), and underweight (WAZ<−2) were computed using WHO standards. Chi‐square and multivariate logistic regression, adjusting for maternal anthropometry and education, child age, and household assets, were conducted. Results Among infants, 20% were stunted, 10% were wasted, 19% were underweight, 28% were ID, and 56% had an elevated CRP. Maternal reports of infant fever (35%) was related to elevated CRP and ID (p< 0.05), cough (28%) and diarrhea (11%) were related to elevated CRP (p< 0.05). In multivariate logistic models, ID (OR: 2.33; CI: 1.15–4.73) and fever (OR: 2.69; CI: 1.31–5.49) were related to wasting, and elevated CRP was related to stunting (OR=1.96; CI: 1.18–3.25) Conclusion Findings suggest that caregiver reports of recent infant morbidity relate to biomarkers of inflammation and ID, and that all three relate to poor growth in infants Research Support; Mathile Institute & Micronutrient Initiative
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".