Muscle mass and association to quality of life in non‐small cell lung cancer patients
Bibliographic record
Abstract
Abstract Background Cancer wasting is characterized by muscle loss and may contribute to fatigue and poor quality of life (QoL). Our aim was to investigate associations between skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD) and selected QoL outcomes in advanced non‐small cell lung cancer (NSCLC) at diagnosis. Methods Baseline data from patients with stage IIIB/IV NSCLC and performance status 0–2 enrolled in three randomized trials of first‐line chemotherapy (n = 1305) were analysed. Associations between SMI (cm2/m2) and SMD (Hounsfield units) based on computed tomography‐images at the third lumbar level and self‐reported physical function (PF), role function (RF), global QoL, fatigue, and dyspnoea were investigated by linear regression using flexible non‐linear modelling. Results Complete data were available for 734 patients, mean age 65 years. Mean SMI was 47.7 cm2/m2 in men (n = 420) and 39.6 cm2/m2 in women (n = 314). Low SMI values were non‐linearly associated with low PF and RF (men P = 0.016/0.020, women P = 0.004/0.012) and with low global QoL (P = 0.001) in men. Low SMI was significantly associated with high fatigue (P = 0.002) and more pain (P = 0.015), in both genders, but not with dyspnoea. All regression analyses showed poorer physical outcomes below an SMI breakpoint of about 42–45 cm2/m2 for men and 37–40 cm2/m2 for women. In both genders, poor PF and more dyspnoea were significantly associated with low SMD. Conclusions Low muscle mass in NSCLC negatively affects the patients' PF, RF, and global QoL, possibly more so in men than in women. However, muscle mass must be below a threshold value before this effect can be detected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".