Associations of linear growth and weight gain in the first 2 years with bone mass at 4 years of age in children in Dhaka, Bangladesh
Bibliographic record
Abstract
Abstract Objective: Growth faltering is widespread in many low- and middle-income countries, but its effects on childhood bone mass accrual are unknown. The objective of this study was to estimate associations between length (conditional length-for-age z-scores, cLAZ) and weight (conditional weight-for-age z-scores, cWAZ) gain in three age intervals (ages 0–6, 6–12 and 12–24 months) with dual-energy X-ray absorptiometry-derived measures of bone mass (total body less head (TBLH) bone mineral content (BMC), areal bone mineral density (aBMD) and bone area) at 4 years of age. Design: Associations between interval-specific growth parameters (cLAZ and cWAZ) and bone outcomes were estimated using linear regression models, adjusted for maternal, child and household characteristics. Setting: Data collection occurred in Dhaka, Bangladesh. Participants: 599 healthy children enrolled in the BONe and mUScle Health in Kids Study. Results: cLAZ in each age interval was positively associated with TBLH BMC, aBMD and bone area at 4 years; however, associations attenuated towards null upon adjustment for concurrent height-for-age z-scores (HAZ) at age 4 years and confounders. cWAZ from 0 to 6 and 6 to 12 months was not associated with bone mass, but every sd increase in cWAZ between 12 and 24 months was associated with greater BMC (7·6 g; 95 % CI: 3·2, 12·0) and aBMD (0·008 g/cm2; 95 % CI: 0·003, 0·014) after adjusting for concurrent WAZ, HAZ and confounders. Conclusions: Associations of linear growth (birth to 2 years) with bone mass at age 4 years were explained by concurrent HAZ. Weight gain in the second year of life may increase bone mass independently of linear growth in settings where growth faltering is common.
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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 | 0.001 |
| 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 | 0.001 | 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".