Cancer Cachexia in the Age of Obesity: Skeletal Muscle Depletion Is a Powerful Prognostic Factor, Independent of Body Mass Index
Bibliographic record
Abstract
PURPOSE: Emerging evidence suggests muscle depletion predicts survival of patients with cancer. PATIENTS AND METHODS: At a cancer center in Alberta, Canada, consecutive patients with cancer (lung or GI; N = 1,473) were assessed at presentation for weight loss history, lumbar skeletal muscle index, and mean muscle attenuation (Hounsfield units) by computed tomography (CT). Univariate and multivariate analyses were conducted. Concordance (c) statistics were used to test predictive accuracy of survival models. RESULTS: Body mass index (BMI) distribution was 17% obese, 35% overweight, 36% normal weight, and 12% underweight. Patients in all BMI categories varied widely in weight loss, muscle index, and muscle attenuation. Thresholds defining associations between these three variables and survival were determined using optimal stratification. High weight loss, low muscle index, and low muscle attenuation were independently prognostic of survival. A survival model containing conventional covariates (cancer diagnosis, stage, age, performance status) gave a c statistic of 0.73 (95% CI, 0.67 to 0.79), whereas a model ignoring conventional variables and including only BMI, weight loss, muscle index, and muscle attenuation gave a c statistic of 0.92 (95% CI, 0.88 to 0.95; P < .001). Patients who possessed all three of these poor prognostic variables survived 8.4 months (95% CI, 6.5 to 10.3), regardless of whether they presented as obese, overweight, normal weight, or underweight, in contrast to patients who had none of these features, who survived 28.4 months (95% CI, 24.2 to 32.6; P < .001). CONCLUSION: CT images reveal otherwise occult muscle depletion. Patients with cancer who are cachexic by the conventional criterion (involuntary weight loss) and by two additional criteria (muscle depletion and low muscle attenuation) share a poor prognosis, regardless of overall body weight.
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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 | 0.002 |
| 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".