Muscle strength cut-points for metabolic syndrome detection among adults and the elderly from Brazil
Bibliographic record
Abstract
We aimed to determine cut-points for muscle strength based on metabolic syndrome diagnosis. This cross-sectional analysis comprised data from 2 cohorts in Brazil (EpiFloripa Adult, n = 626, 44.0 ± 11.1 years; EpiFloripa Aging, n = 365, 71.6 ± 6.1 years). Metabolic syndrome was assessed by relative handgrip strength (kgf/kg). Metabolic syndrome was defined as including ≥3 of the 5 metabolic abnormalities according to the Joint Interim Statement. Optimal cut-points from Receiver Operating Characteristic (ROC) curves were determined. Adjusted logistic regression was used to test the association between metabolic syndrome and the cut-points created. The cut-point identified for muscle strength was 1.07 kgf/kg (Youden index = 0.310; area under the curve (AUC)) = 0.693, 95% CI 0.614–0.764) for men and 0.73 kgf/kg (Youden index = 0.481; AUC = 0.768, 95% confidence interval (CI) = 0.709–0.821) for women (age group 25 to < 50 years). The best cut-points for men and women aged 50+ years were 0.99 kgf/kg (Youden index = 0.312; AUC = 0.651; 95% CI = 0.583–0.714) and 0.58 kgf/kg (Youden index = 0.378; AUC = 0.743; 95% CI = 0.696–0.786), respectively. Cut-points derived from ROC analysis have good discriminatory power for metabolic syndrome among adults aged 25 to <50 years but not for adults aged 50+ years. Novelty: First-line management recommendation for metabolic syndrome is lifestyle modification, including improvement of muscle strength. Cut-points for muscle strength levels according to sex and age range based on metabolic syndrome were created. Cut-points for muscle strength can assist in the identification of adults at risk for cardiometabolic disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".