LOW MUSCLE MASS AND LOW MUSCLE STRENGTH FOR THE PREDICTION OF ADVERSE OUTCOMES AMONG COMMUNITY-DWELLING OLDER PEOPLE
Bibliographic record
Abstract
According to recent guidelines, diagnosis of sarcopenia requires the simultaneous evaluation of muscle mass and function. We studied the individual contribution of low muscle mass (LMM) and low muscle strength (LMS) in predicting incident mortality and disability in a cohort of community-dwelling older people participating in the InCHIANTI Study. LMM was defined according to skeletal muscle index (obtained from bioelectrical impedance analysis) and LMS was determined measuring grip strength. Study outcomes were death and 3-year incident disability (defined as loss of ability to walk 400mt or reduction in speed in the worst quartile). Five-hundred-thirty-two participants (mean age 76.9 years, women 53.4%) were included in the analysis. In an unadjusted model, people who had LMM, LMS or both showed higher mortality risk compared to the fit group (hazard ratio [HR] 2.99, 95% C.I. 1.4–6.41; 4.45, 95% C.I. 1.97–10.07 and 3.46, 95% C.I. 1.4–8.55, respectively). After adjusting for sex, age and chronic comorbidities, LMM and LMS, but not their combination, were still associated with mortality. The positive predictive values were 0.08, 0.17, 0.23 for LMM, LMS and their combination, respectively; the negative predictive value was 0.97. None of the indices was significantly associated with 3-year disability (relative risk 0.89, 95% C.I. 0.6–1.29; 1.14, 95% C.I. 0.51–2.2; 1.32, 95% C.I. 0.65–2.4, respectively). In conclusion, the presence of LMM or LMS seems to be associated with an higher mortality risk, but their combination does not. These indices may be useful to identify people at low risk of adverse outcomes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".