COMPARING SUBJECTS EWGSOP SARCOPENIC STATUS AND THEIR CLINICAL FRAILTY SCALE LEVEL
Bibliographic record
Abstract
The European Working Group of Sarcopenia in Older People (EWGSOP) classifies normal, presarcopenia, sarcopenia, and severe sarcopenia depending on lean muscle mass, grip strength and gait speed. The Clinical Frailty Scale (CFS) has 9 classifications. Prevalence of sarcopenia and frailty increase with age. Are they two sides of the same coin? Seniors participating in an exercise study were evaluated for sarcopenic status. Blinded to this information, they were evaluated using the CFS and classified accordingly. Data was obtained from 39 participants (6 men), average age 75.7years (67–90). Average MMSE 29.1 (22–30), MoCA 26.4 (18–30). 11 were normal, 11 were obese, the remainder various stages of sarcopenia. 24 were CFS 3 or higher. Poor correlation was found between EWGSOP sarcopenic status and CFS (R=0.43), lean muscle mass (appendicular lean mass/height2) and CFS (R=0.21 in women), EWGSOP grip strength cut-offs and CFS (R=0.46). However, good correlation was found between CFS and 6m absolute walk time (R=0.82) and gait speed (R=-0.61). This study is limited by fewer individuals in the sarcopenic or frail spectrum. This study suggests there is poor correlation between sarcopenic status (as defined by EWGSOP criteria), absolute muscle mass or grip strength and CFS. However, there was good correlation with gait time and speed, suggesting that functional measures of muscle are more important than absolute muscle mass in the development of frailty. Sarcopenia, as defined by EWGSOP does not equate to frailty as defined by CFS. The use of standardized definitions has important implications for research into potential therapeutic interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".