Association between upper leg length and metabolic syndrome among US elderly participants-results from the NHANES (2009-2010).
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between upper leg length (ULL) and metabolic syndrome (MetS) in older adults. METHODS: Data was collected from National Health and Nutritional Examination Survey (NHANES, 2009-2010). 786 individuals (385 males and 401 females) who were 60 years of age or older were included in this analysis. MetS was defined as having at least three of following conditions, i.e., central obesity, dyslipidemia, insulin resistance, and hypertension based on National Cholesterol Education Program guidelines. ULL was grouped into gender-specific tertiles. RESULTS: 328 (41.7%) of participants were categorized as having MetS (38.7% in men and 49.1% in women, P = 0.002). Compared to individuals in the 1(st) tertile (T1) of ULL, those in the 3(rd) tertile (T3) had lower levels of triglycerides (120.8 vs. 153.1 mg/dL, P = 0.045), waist circumference (100.7 vs. 104.2 cm, P = 0.049), and systolic blood pressure (126.7 vs. 131.4 mmHg, P = 0.005), but higher levels of high-density-lipoprotein cholesterol (58.1 vs. 52.4 mg/dL, P = 0.024). The odds ratios (95% CI) of MetS from multivariate logistic regression were 0.57 (0.32-1.03) for individuals in the T2 of ULL and 0.39 (0.24-0.64) for individuals in the T3 of ULL, respectively (P-value for the trend 0.022). CONCLUSIONS: ULL was negatively associated with MetS in older adults. Further research is needed to identify potential mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".