Behavioral risk factors in relation to visceral adipose tissue deposition in adolescent females
Bibliographic record
Abstract
OBJECTIVE: To characterize visceral adipose tissue (VAT) and subcutaneous abdominal adipose tissue (SAAT) deposition in girls over the pubertal period and to assess the influence of behavioral risk factors on their deposition. PARTICIPANTS: In total, 41 subjects of mean age of 13.5 years (standard deviation, SD=0.9) were assessed at menarche. At 4 years after menarche, follow-up data were available for 24 of these subjects. METHODS: VAT and SAAT were measured by magnetic resonance imaging (MRI) and total body fat by isotopic dilution of (18)O water at menarche and 4 years after menarche in a subset of subjects enrolled in a larger study of growth and development. Smoking, alcohol use, and physical activity were assessed by self-report at both time points. Smoking, alcohol use, and physical activity at 4 years after menarche were assessed in relation to concurrent VAT and SAAT, and to the 4-year change in VAT and SAAT. RESULTS: Smoking and alcohol use at 4 years after menarche was associated with the change in VAT over the 4-year period, before (p<0.03 and p<0.02, respectively), and after adjustment for total body fat (p<0.01 and p<0.02, respectively). CONCLUSIONS: In addition to the established health risks, smoking and drinking, even at low levels, appear to be associated with increased VAT deposition in adolescent females.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".