Whole body lean mass at birth significantly tracks to 12 and 36 months of age
Bibliographic record
Abstract
Childhood is a critical period of musculoskeletal development. Body composition in childhood is predicted by birth weight, but this relationship has not been studied using neonatal body composition in healthy term born infants of appropriate weight for gestational age. The objective of this study was to determine which indices of body composition track best from birth. Twenty‐five children (16 male, 9 female) had weight (wt) and whole body composition measured using dual‐energy x‐ray absorptiometry (Hologic 4500A Discovery Series with infant and pediatric software, Bedford, MA, USA) at 1, 12, and 36 mo of age. Correlation analyses were performed for indices of body composition at 1 mo compared to 12 and 36 mo and significance set at p<0.05. Body wt (g) at 1 mo was related to wt at 12 mo (r=0.61; P<0.001) and 36 mo (r=0.39; P<0.01); wt at 12 mo was also related to wt at 36 mo (r=0.77; P<0.001). Whole body lean mass (LM; g) at 1 mo of age was related to wt (r=0.63; P<0.001) and LM (r=0.51; P<0.01) at 12 mo plus wt (r=0.48; P<0.01) and LM (r=0.50; P<0.001) at 36 mo. At 12 mo, LM related with wt (r=0.63; P<0.05), LM (r=0.73; P<0.001) and % fat (r=−0.50; P<0.05) at 36 mo. Fat mass at 1 mo was not related to subsequent measures, however values at 12 and 36 mo were related (r=0.48; P<0.05). These results suggest lean muscle mass established by 1 mo of age is a consistent indicator of future body composition indices during early development. Funded by CIHR.
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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 | 0.001 | 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 | 0.001 |
| 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".