Cancer‐related fatigue: the impact of skeletal muscle mass and strength in patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Although exertional fatigue is directly and negatively related to skeletal muscle mass and strength, it is currently unknown if these variables are associated with cancer-related fatigue (CRF). Therefore, the purpose of this study was to determine if CRF is associated with measures of appendicular lean muscle mass and strength in advanced cancer patients (ACP). METHODS AND RESULTS: Eighty-four patients (48 men, 36 women aged 61.6 ± 13.2 year) newly diagnosed (≤6 months) with inoperable (Stages III-IV) gastrointestinal or non-small cell lung cancer participated in this study. All patients completed the Brief Fatigue Inventory (BFI). Handgrip (HGS) and quadriceps (QS) strength were assessed using isometric and isokinetic dynamometry, respectively. Skeletal muscle mass index (SMMI) was calculated from the appendicular lean mass measured via dual-energy X-ray absorptiometry divided by body height squared. Univariate analysis showed BFI to be significantly associated with body mass index, weight loss, anemia, hypoalbuminemia, activity level, pain, depression, and sarcopenia along with SMMI, HGS, and QS. HGS (r = -0.34; p = 0.018), QS (r = -0.39; p = 0.024), and SMMI (r = -0.60; p < 0.001) were negatively correlated with BFI total scores in men but not in women. When adjusted for sex, age, diagnosis, survival, along with the above characteristics, multivariate analyses showed that BFI scores were negatively associated with HGS (B = -0.90; 95% CI -1.5:-0.3), QS (-0.2; -0.3:-0.01), and SMMI (-7.5; -13.0:-2.0). There was a significant sex × SMMI interaction (10.8; 1.2:20.5), where BFI decreased with increasing SMMI in men, but did not change with SMMI in women. CONCLUSION: These results suggest that in ACP, CRF is related to muscle mass and strength, which may provide targets for future 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".