Is abdominal adiposity in healthy Sri Lankan neonates different from the rest of the world?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adiposity at birth is a predictor of childhood obesity. Abdominal circumference (AC) at birth has been shown to correlate well with visceral adipose tissue and abdominal subcutaneous adipose tissue. Adiposity differs according to ethnicity and geography. The aim of this study was to describe the anthropometry derived adiposity phenotype in neonates from Colombo, Sri Lanka and compare it with global data. METHODS AND STUDY DESIGN: Birth anthropometry was performed within 12-24 hours by the same investigator as part of a prospective cohort study on healthy term babies, at a tertiary care hospital in Colombo, Sri Lanka, 2015-2019. The anthropometry derived adiposity phenotype was indicated by skinfold thickness, AC and upper arm fat area (UFA) derived from the mid-upper arm circumference (MUAC). RESULTS: Sri Lankan neonates had a significantly lower weight with significantly higher AC (n=337, 2.9±0.4 kg, 30.6±2.3 cm) compared to Canadian (n=389, 3.5±0.02 kg, 29.9±2.1 cm; p<0.001) and Australian (n=1270, 3.4±0.4 kg, 28.5±1.9 cm; p<0.001) neonates. Anthropometry derived adiposity at birth showed a significant correlation with weight and BMI of both mother and father (p<0.05) as opposed to their income or education (p>0.05). CONCLUSIONS: Healthy neonates from Colombo, Sri Lanka demonstrated significantly higher AC despite significantly lower weight, indicating increased abdominal adiposity compared to neonates from high-income countries as well as Indian neonates with the thin-fat phenotype.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".