Cancer‐Associated Malnutrition and CT‐Defined Sarcopenia and Myosteatosis Are Endemic in Overweight and Obese Patients
Bibliographic record
Abstract
BACKGROUND: Overweight/obese patients' large fat mass can mask the loss of skeletal muscle, which is associated with mortality in the oncology setting. We investigated the prevalence of computed tomography (CT)-defined sarcopenia and myosteatosis across different levels of nutrition risk assessed by the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF). We also evaluated whether the PG-SGA SF, sarcopenia, and myosteatosis were prognostic of overall survival. METHODS: with newly diagnosed head and neck cancer (any stage) or lung and gastrointestinal tract cancer (locally recurrent or metastatic) were screened at presentation to oncology clinics. Nutrition risk was assigned based on PG-SGA SF triage recommendations. Based on CT, patients were classified with sarcopenia and/or myosteatosis using published cutoffs. Survival analyses were conducted. RESULTS: Patients (n=1157) were 63.6 ± 11.4 years, 64% male, and 61% had stage IV disease. Sarcopenia and myosteatosis were prevalent across PG-SGA SF nutrition risk categories (scores 0-1 [no risk; 36% sarcopenic; 44% myosteatotic], scores 2-3 [37%; 37%], scores 4-8 [40%; 41%], and scores ≥9 [high risk; 50%; 49%]). In multivariable survival analysis, PG-SGA SF scores ≥9 (hazard ratio [HR] 2.08, 95% confidence interval [CI] 1.66-2.60, P<0.001), sarcopenia (HR 1.25, 95% CI 1.06-1.46, P=0.006), and myosteatosis (HR 1.25, 95% CI 1.07-1.46, P<0.001) independently predicted reduced survival. CONCLUSION: CT-defined sarcopenia and myosteatosis are prevalent across different levels of nutrition risk in overweight/obese patients with cancer. Assessment of skeletal muscle using CT adds prognostic value to the PG-SGA SF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".