Skeletal muscle measures and physical function in older adults with cancer: sarcopenia or myopenia?
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent in older adults and linked with adverse outcomes in cancer, yet the definition and role of sarcopenia remains uncertain. The aim of this study was to examine the association of Computerized Tomography (CT) assessed skeletal muscle measures with physical function in older adults with cancer. RESULTS: CTs for 185 patients were available. Median age 73 (IQR 68-76) and 56.5% female. After controlling for sex and BMI, we found no evidence that SMI was associated with physical function impairments. Both SMD and SMG were associated physical function impairments and higher values were associated with decreased limitations in instrumental activities of daily living (RR 0.84 [CI 0.73-0.96] and 0.94 [CI 0.89-0.99], respectively), climbing stairs (RR 0.84 [CI 0.76-0.94] and 0.91 [CI 0.87-0.96]), walking 1 block (RR 0.77 [CI 0.67-0.90] and 0.91 [CI 0.85-0.97]), and prolonged Timed Up and Go (RR 0.83 [CI 0.75-0.92] and 0.92 [CI 0.88-0.96]). MATERIALS AND METHODS: Using the Carolina Senior Registry, we identified patients with CT imaging performed within 60 days +/- of baseline geriatric assessment (GA). Skeletal muscle area and density (SMD) were analyzed from L3 lumbar segments. Muscle area and height (m2) were used to calculate skeletal muscle index (SMI). Skeletal Muscle Gauge (SMG) was created by multiplying SMI x SMD. CONCLUSIONS: Skeletal muscle mass as assessed from CT imaging was not associated with physical function impairments. Skeletal muscle radiodensity was more associated with physical function and may aid in identifying older adults at risk for functional impairments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".