Interrelationships of growth, development and biomarkers of iron status
Bibliographic record
Abstract
Objective To study the relationships between a set of observed variables and a set of continuous latent variables among growth, development and iron statues in rural infants using confirmatory factor analysis. Methods Z scores of height for age, weight for age and arm mid‐upper arm circumference for age were calculated. Motor, language and visual reception were in 497 infants of 6–12 months using Mullen Scales of Early Learning. Indicators of iron status, hemoglobin, ferritin and sTfR were analyzed. Comparative fit index (CFI), goodness of fit index (GFI), normed fit index (NFI) and root mean square error of approximation (RMSEA) were used for testing the model. Results CFI (0.962), GFI (0.966) and NFI (0.935) values met the criteria for an acceptable model fit ¡Ý0.90. The RMSEA obtained was 0.052 which is close to a cut‐off value of ¡Ü0.05 suggesting a good fit. The relationship between development, and indictors of iron status was significant r=0.130 (p<0.05). Fine motor and visual reception showed higher loadings for development. Other contributing variables were found to be weight and sTfR. Conclusions The results suggest that the model is valid to establish the interaction between iron status and development. 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 | 0.009 |
| 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) | 0.001 | 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".