Utility of published skinfold thickness equations for prediction of body composition in very young New Zealand children
Bibliographic record
Abstract
Measurement of body composition is increasingly important in research and clinical settings but is difficult in very young children. Bioelectrical impedance analysis (BIA) and air displacement plethysmography (ADP) are well-established but require specialist equipment so are not always feasible. Our aim was to determine if anthropometry and skinfold thickness measurements can be used as a substitute for BIA or ADP for assessing body composition in very young New Zealand children. We used three multi-ethnic cohorts: 217 children at a mean age of 24·2 months with skinfold and BIA measurements; seventy-nine infants at a mean age of 20·9 weeks and seventy-three infants at a mean age of 16·2 weeks, both with skinfold and ADP measurements. We used Bland-Altman plots to compare fat and fat-free mass calculated using all potentially relevant equations with measurements using BIA or ADP. We also calculated the proportion of children in the same tertile for measured fat or fat-free mass and tertiles (i) calculated using each equation, (ii) each absolute skinfold, and (iii) sum of skinfold thicknesses. We found that even for the best equation for each cohort, the 95 % limits of agreement with standard measures were wide (25-200 % of the mean) and the proportion of children whose standard measures fell in the same tertile as the skinfold estimates was ≤69 %. We conclude that none of the available published skinfold thickness equations provides good prediction of body composition in multi-ethnic cohorts of very young New Zealand children with different birth history and growth patterns.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".