WHO’S SARCOPENIC? AN ANALYSIS USING THE CANADIAN LONGITUDINAL STUDY ON AGING DATA
Bibliographic record
Abstract
Abstract A limitation of the sarcopenia definitions used in our analyses is the use of DXA measured ALM to approximate muscle mass. Although many consider ALM to be the reference standard for measuring muscle mass for sarcopenia, it is actually a measure of lean mass that includes organ tissue, water, and all other non-bone and non-fat soft tissues in addition to muscle mass. The results may be substantially altered if more accurate measures of muscle mass such as the D3-creatine method were used. We have reported that there is generally slight to moderate agreement (Cohen’s κ values of.00–.60) between most of the combinations of variables used to ascertain sarcopenia status recommended by the expert group definitions. For definitions using lean mass, the agreement between different adjustment techniques for lean mass ranged from slight to substantial. These findings were consistent across the range of cutoffs for each variable that are either observed in the literature or recommended by the expert group definitions for sarcopenia. The general lack of agreement between sarcopenia definitions observed in our study underscores the importance of the sarcopenia research community identifying a unified ascertainment method for sarcopenia. Having multiple definitions of sarcopenia identifying different groups of individuals as sarcopenic may hinder vital research developing clinical management strategies for sarcopenia by decreasing the comparability between studies.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".