A comparison of three infant skinfold reference standards: <scp>T</scp>anner–<scp>W</scp>hitehouse, <scp>C</scp>ambridge <scp>I</scp>nfant <scp>G</scp>rowth Study, and <scp>WHO C</scp>hild <scp>G</scp>rowth <scp>S</scp>tandards
Bibliographic record
Abstract
As researchers increasingly focus on early infancy as a critical period of development, there is a greater need for methodological tools that can address all aspects of infant growth. Infant skinfold measures, in particular, are measurements in need of reliable reference standards that encompass all ages of infants and provide an accurate assessment of the relative fatness of a population. This report evaluates three published reference standards for infant skinfold measurements: Tanner-Whitehouse, Cambridge Infant Growth Study, and the World Health Organization (WHO) Child Growth Standards. To assess these standards, triceps skinfolds from a population of rural Kenyan infants (n = 250) and triceps skinfolds and subscapular skinfolds from infants in the National Health and Nutrition Examination Survey 1999-2002 (NHANES; n = 1197) were calculated as z-scores from the lambda-mu-sigma curves provided by each reference population. The Tanner-Whitehouse standards represented both the Kenyan and US populations as lean, while the Cambridge standards represented both populations as overfat. The distribution of z-scores based on the WHO standards fell in the middle, but excluded infants from both populations who were below the age of 3 months. Based on these results, the WHO reference standard is the best skinfold reference standard for infants over the age of 3 months. For populations with infants of all ages, the Tanner-Whitehouse standards are recommended, despite representing both study populations as underfat. Ideally, the WHO will extend their reference standard to include infants between the ages of 0 and 3 months.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".