44THE DIVERSITY OF A GROUP OF COMMUNITY DWELLING EXERCISE STUDY PARTICIPANTS WHEN CLASSIFIED BY EWGSOP CRITERIA FOR SARCOPENIA AND SARCOPENIC OBESITY
Bibliographic record
Abstract
Introduction: Prevalence of sarcopenia increases with age. Sarcopenia subtypes are defined by the European Working Group of Sarcopenia in Older People (EWGSOP), based on presence/absence of low muscle mass, plus/minus low muscle strength or low performance. Methods: Participants are over 65 years, independently mobile, community dwelling, enrolled in a twelve month study evaluating exercise. Baseline assessments included dual energy X-ray absorptiometry (DXA) body composition (BC) analysis, grip strength (dynamometer) and gait speed (10 metre walk test). BC provided appendicular lean mass/height2 (aLM/ht2) and percentage body fat. Data evaluated per EWGSOP guidelines, using cut offs: aLM/ht2 of <5.67 (women) <7.26 (men); grip strength of <20 (women), <30 (men); and gait speed <1m/s for both. Low grip strength and gait speed, with normal aLM/ht2 were classified as “weak,” to differentiate them from normal. Prescaropenics had only low aLM/ht2, sarcopenics had low aLM/ht2 plus one abnormal level in one of the other parameters, and severe sarcopenics had abnormal levels in all parameters. Obesity was defined by DXA BC percentage fat of >40% (women), >28% (men).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".