Exploring potential predictors of low muscle mass and muscle loss in adults with cancer: A scoping review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Early identification of cancer-related muscle loss is essential to enable timely interventions and mitigate adverse outcomes, including mortality. This scoping review aimed to identify routinely assessed clinical measures associated with low muscle mass or muscle loss to inform future global screening and assessment. METHODS: Medline Complete, CINAHL Complete and Embase databases were screened from January 2000 to October 2024. Eligible studies investigated factors associated with cancer-related muscle loss, included adults undergoing or previously treated for cancer, and assessed or estimated muscle mass. RESULTS: The search identified 22,270 studies, of which 292 were included. Most involved patients with upper and/or lower gastrointestinal cancers (50 %), undergoing surgery (44 %) or chemotherapy (27 %). Two-thirds (65 %) assessed muscle mass using computed tomography (CT) at the third lumbar vertebra. Other methods included CT-defined muscle mass of single muscles (e.g., psoas) (15 %), bioelectrical impedance analysis or spectroscopy (12 %), dual-energy x-ray absorptiometry (DXA) (7 %) or other (3 %). As the benchmark for muscle mass assessment in oncology, results focused on CT-defined muscle mass, with comparison to other methods. Twenty factors were identified. Thirteen showed a consistent association in unadjusted and/or adjusted analysis: age, body mass index (BMI), performance status, muscle strength, physical function, arm and leg circumference, body weight, body fat, weight loss, fatigue, energy or protein intake, and physical inactivity. CONCLUSIONS: This review identified 13 factors consistently associated with CT-defined muscle loss which may help identify patients with cancer who are at risk and require further assessment and timely referral for evidence-based nutrition and exercise interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".