Predicting Slow Walking Speed From a Pooled Cohort Analysis: Sarcopenia Definitions, Agreement, and Prevalence in Australia and New Zealand
Bibliographic record
Abstract
BACKGROUND: Recent operational definitions of sarcopenia have not been replicated and compared in Australia and New Zealand (ANZ) populations. We aimed to identify sarcopenia measures that discriminate ANZ adults with slow walking speed (<0.8 m/s) and determine the agreement between the Sarcopenia Definitions and Outcomes Consortium (SDOC) and revised European Working Group for Sarcopenia in Older People (EWGSOP2) operational definitions of sarcopenia. METHODS: Eight studies comprising 8 100 ANZ community-dwelling adults (mean age ± standard deviation, 62.0 ± 14.4 years) with walking speed, grip strength (GR), and lean mass data were combined. Replicating the SDOC methodology, 15 candidate variables were included in sex-stratified classification and regression tree models and receiver operating characteristic curves on a pooled cohort with complete data to identify variables and cut points discriminating slow walking speed (<0.8 m/s). Agreement and prevalence estimates were compared using Cohen's Kappa (CK). RESULTS: Receiver operating characteristic curves identified GR as the strongest variable for discriminating slow from normal walking speed in women (GR <20.50 kg, area under curve [AUC] = 0.68) and men (GR <31.05 kg, AUC = 0.64). Near-perfect agreement was found between the derived ANZ cut points and SDOC cut points (CK 0.8-1.0). Sarcopenia prevalence ranged from 1.5% (EWGSOP2) to 37.2% (SDOC) in women and 1.0% (EWGSOP2) to 9.1% (SDOC) in men, with no agreement (CK <0.2) between EWGSOP2 and SDOC. CONCLUSIONS: Grip strength is the primary discriminating characteristic for slow walking speed in ANZ women and men, consistent with findings from the SDOC. Sarcopenia Definitions and Outcomes Consortium and EWGSOP2 definitions showed no agreement suggesting these proposed definitions measure different characteristics and identify people with sarcopenia differently.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".