Integrated impact of multiple body composition parameters on overall survival in gastrointestinal or genitourinary cancers: A descriptive cohort study
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate if combining low muscle mass with additional body composition abnormalities, such as myosteatosis or adiposity, could improve survival prediction accuracy in a large cohort of gastrointestinal and genitourinary malignancies. METHODS: In total, 2015 patients with surgically-treated gastrointestinal or genitourinary cancer were retrospectively analyzed. Skeletal muscle index, skeletal muscle radiodensity, and visceral/subcutaneous adipose tissue index were determined. The primary outcome was overall survival determined by hospital records. Multivariate Cox hazard models were used to identify independent predictors for poor survival. C-statistics were assessed to quantify the prognostic capability of the models with or without incorporating body composition parameters. RESULTS: Survival curves were significantly demarcated by all 4 measures. Skeletal muscle radiodensity was associated with non-cancer-related deaths but not with cancer-specific survival. The survival outcome of patients with low skeletal muscle index was poor (5-year OS; 65.2%), especially when present in combination with low skeletal muscle radiodensity (5-year overall survival; 50.2%). All examined body composition parameters were independent predictors of lower overall survival. The model for predicting overall survival without incorporating body composition parameters had a c-index of 0.68 but increased to 0.71 with the inclusion of low skeletal muscle index and 0.72 when incorporating both low skeletal muscle index and low skeletal muscle radiodensity/visceral adipose tissue index/subcutaneous adipose tissue index. CONCLUSION: Patients exhibiting both low skeletal muscle index and other body composition abnormalities, particularly low skeletal muscle radiodensity, had poorer overall survival. Models incorporating multiple body composition prove valuable for mortality prediction in oncology settings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".