Review of the Interaction Between Body Composition and Clinical Outcomes in Metastatic Renal Cell Cancer Treated With Targeted Therapies
Bibliographic record
Abstract
Treatment of metastatic renal cell cancer (mRCC) currently focuses on inhibition of the vascular endothelial growth factor pathway and the mammalian target of rapamycin (mTOR) pathway. Obesity confers a higher risk of RCC. However, the influence of obesity on clinical outcomes in mRCC in the era of targeted therapy is less clear. This review focuses on the impact of body composition on targeted therapy outcomes in mRCC. The International Metastatic Renal Cell Carcinoma Database Consortium database has the largest series of patients evaluating the impact of body mass index (BMI) on outcomes in mRCC patients treated with targeted therapy. Overall survival was significantly improved in overweight patients (BMI ≥ 25 kg/m2), and this observation was externally validated in patients who participated in Pfizer trials. In contrast, sarcopenia is consistently associated with increased toxicity to inhibitors of angiogenesis and mTOR. Strengthening patients with mRCC and sarcopenia, through a structured exercise program and dietary intervention, may improve outcomes in mRCC treated with targeted therapies. At the same time, the paradox of obesity being a risk factor for RCC while offering a better overall survival in response to targeted therapy needs to be further evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".