Prognostic role of body composition parameters in renal cell carcinoma (RCC).
Bibliographic record
Abstract
4621 Background: Previous studies have shown that body component i.e. muscle tissue (MT) and adipose tissue (AT) are linked to overall survival (OS) and progression free survival (PFS). The aim of our study is to analyze whether MT and AT have a prognostic role in metastatic RCC treated with targeted therapy. Methods: We investigated body mass index (BMI), MT and AT in RCC pts. Analysis of CT image was used to evaluate cross-sectional areas (cm2) of total AT (TAT), MT, and the grey level image (GLI) of MT, reflecting physical properties of the scanned tissue and used as a proxy to describe the quality of muscle. The 3rd lumbar vertebra (L3) was chosen as a landmark since L3 and whole-body measurements are linearly related. Images were analyzed using Slice-O-Matic software V4.3 (Tomovision). Indexed on height MT (cm2/m2), indexed on height TAT (cm2/m2) and mean GLI of MT were computed and described stratified on sex. For each measurement, population was divided in two groups: patients with values < or >= median observed in patient of the same gender and OS and PFS were estimated using Kaplan-Meier method and compared with the log-rank test. Multivariable Cox proportional hazards model were adjusted for modified MSKCC risk group and treatment (active versus placebo). Results: There were 113 men aged of 60 (interquartile range=52-56) years with BMI of 26 (24-29) kg2/m2 and 36 women aged of 58 (54-65) years with BMI of 23 (20-26) kg2/m2. After adjustment for MSKCC (OS and PFS) and active treatment (PFS), GLI of MT over the median was associated with longer OS (HR=1.85, 95%CI=[1.22;2.82], p=.004) and PFS (HR=1.81, 95%CI=[1.22;2.65], p=.002) Conclusions: Quality of muscular tissue is independently associated with better outcome in RCC. Surprisingly, adipose tissue as well as BMI is not associated with survival in this study. [Table: see text]
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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) | 0.001 | 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".