Associations of Enteric Protein Loss, Vaccine Response, Micronutrient Deficiency, and Maternal Depressive Symptoms with Deviance in Childhood Linear Growth: Results from a Multicountry Birth Cohort Study
Bibliographic record
Abstract
We identified the determinants of positive (children who had a birth weight < 2.5 kg and/or maternal height < 145 cm but were nonstunted at 24 months of age) and negative (children who had a birth weight ≥ 2.5 kg and maternal height ≥ 145 cm but were stunted at 24 months of age) deviance in childhood linear growth. We found that socioeconomic status (β = 1.54, P < 0.01), serum retinol (β = 0.05, P < 0.01), hemoglobin (β = 0.36, P < 0.01), length-for-age Z-score (LAZ) at birth (β = 0.47, P < 0.01), and tetanus vaccine titer (β = 0.182, P < 0.05) were positively and maternal depressive symptom (β = -0.05, P < 0.01), serum ferritin (β = -0.03, P < 0.01), male sex (β = -1.08, P < 0.01), and α1-antitrypsin (β = -0.81, P < 0.01) were negatively associated with positive deviance. Further, diarrhea episodes (β = 0.02, P < 0.01), male sex (β = 0.72, P < 0.01), and α1-antitrypsin (β = 0.67, P < 0.01) were positively and hemoglobin (β= -0.28, P < 0.01), soluble transferrin receptor level (β = -0.15, P < 0.01), and LAZ score at birth (β = -0.90, P < 0.01) were negatively associated with negative deviance. To summarize, enteric protein loss, micronutrient deficiency, vaccine responses and maternal depressive symptoms were associated with linear growth deviance in early childhood. In such a background, public health approaches aimed at reducing the risk of intestinal inflammation and altered gut permeability could prove fruitful in ensuring desired linear growth in children. In addition, maternal mental health issue should also be considered, especially for promoting better nutritional status in children in the context of linear growth deviance.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".