Muscle matters: Prognostic implications of malnutrition and muscle health parameters in patients with cancer. A secondary analysis of a randomised trial
Bibliographic record
Abstract
BACKGROUND: Low muscle mass and malnutrition are independently associated with an increased risk of adverse outcomes in patients with cancer. However, it is not yet clear which parameter is most indicative of these risks. This study investigates the prognostic significance of different parameters reflecting malnutrition and muscle health in a well-characterised oncology population at nutritional risk. METHODS: This preplanned secondary analysis included patients with cancer from a Swiss-wide, randomised-controlled nutritional trial. We investigated associations among malnutrition markers (i.e., malnutrition diagnosis based on modified Global Leadership Initiative on Malnutrition (GLIM) criteria, albumin concentration) and muscle health markers (i.e., hand grip strength, computed tomography (CT)-based muscle mass and radiodensity) with 180-day all-cause mortality (primary outcome). RESULTS: We included 269 patients with a main admission diagnosis of cancer and available CT scans. In a mutually adjusted model, four parameters contributed to risk assessment including modified malnutrition diagnosis (GLIM) (HR 1.78 (95%CI 1.17 to 2.69), p = 0.007, AUC 0.58), low albumin concentration (HR 1.58 (95%CI 1.08 to 2.31), p = 0.019, AUC 0.62), low handgrip strength (HR 2.05 (95%CI 1.43 to 2.93), p = 0.001, AUC 0.62) and low muscle radiodensity (HR 1.39 (95%CI 0.90 to 2.16), p = 0.139, AUC 0.63). Combining these parameters resulted in a model with high prognostic power regarding 180-day mortality (overall AUC 0.71). CONCLUSIONS: In this study of inpatients with cancer at nutritional risk, several malnutrition and muscle health parameters emerged as independent prognostic indicators for mortality. The use of these parameters may improve risk stratification and guide nutritional interventions in this vulnerable population. TRIAL REGISTRATION: ClinicalTrials.gov, number NCT02517476.
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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) | 0.001 | 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) | 0.000 | 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".