The Role of Frailty and Myosteatosis in Predicting All-Cause Mortality in Older Adults with Cancer
Bibliographic record
Abstract
Frailty and myosteatosis are each prognostic of all-cause mortality (ACM) in patients with cancer. However, it is unclear whether myosteatosis adds value to frailty for predicting ACM. We assessed whether myosteatosis improves the predictive ability of frailty for ACM in older adults undergoing chemotherapy. This was a retrospective study of older adults (≥65 years) initiating chemotherapy between June 2015 and June 2022. Frailty was assessed using a 24-item frailty index (FI). Myosteatosis was evaluated via computed tomography scans at the third lumbar vertebra (L3).. Multivariable Cox regression and Uno’s c-statistic determined the predictive performance of the FI and myosteatosis. In total, 115 participants (mean age: 77.1 years) were included. Frailty alone (adjusted hazards ratio (aHR) = 1.68, 95% confidence intervals (CIs) = 1.03–2.72, p = 0.037) and myosteatosis alone (aHR = 2.14, 95%CI = 1.07–4.30, p = 0.032) exhibited similar performance (c-statistic = 0.66) in predicting ACM in multivariable analyses adjusted for age, sex, body mass index, and treatment intent. However, the highest predictive performance for ACM was observed after inclusion of both myosteatosis and frailty in the multivariable model (c-statistic = 0.70). Myosteatosis improves the performance of frailty for predicting ACM in older adults with cancer. Prospective studies to assess the effect of exercise on myosteatosis in older patients are warranted.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".