Subcutaneous adiposity is an independent predictor of mortality in cancer patients
Bibliographic record
Abstract
BACKGROUND: ) does not provide an appropriate assessment of body composition; therefore, the concept of the 'obesity paradox' needs to be investigated based on the prognostic significance of fat and muscle. Independent prognostic significance of adipose tissue in predicting mortality, importance of visceral and subcutaneous adiposity in the presence and absence of sarcopenia on survival, was investigated. METHODS: Adiposity markers including total adipose index (TATI), visceral adipose tissue index (VATI) and subcutaneous adipose tissue index (SATI) were estimated for 1473 gastrointestinal and respiratory cancer patients and 273 metastatic renal cell carcinoma patients using computed tomography. Univariate and multivariate analysis to determine mortality hazard ratios (HR) were conducted using cox proportional hazard models. RESULTS: in females) independently associated with increased mortality (HR: 1.26; 95% CI: 1.11-1.43; P<0.001) and shorter survival (13.1 months; 95% CI, 11.4-14.7) compared to patients with high SATI (19.3 months; 95% CI, 17.6-21.0; P<0.001). In the presence of sarcopenia, the longest survival was observed in patients with high subcutaneous adiposity. CONCLUSIONS: Subcutaneous adipose tissues appear to associate with reduction in mortality risk demonstrating the prognostic importance of fat distribution. The effect of sarcopenia on survival was more pronounced in patients with low subcutaneous adiposity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".