Influence of abdominal adiposity, waist circumference, and body mass index on clinical and pathologic findings in patients treated with radiotherapy for localized prostate cancer
Bibliographic record
Abstract
BACKGROUND: Increased body mass index (BMI) has been associated with more aggressive prostate cancer (PC). The relation among abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), waist circumference (WC), and BMI was compared with clinical and pathologic findings in patients treated with radiotherapy for localized PC. METHODS: VAT, SAT, WC (all measured by planning abdominopelvic computed tomography scan) and BMI were compared with clinical and pathologic factors using univariate analyses. Cox regression analyses were performed to evaluate whether obesity measures significantly predicted risk for secondary malignancies. RESULTS: Of 276 analyzed patients, 80 (29%) were obese (BMI ≥ 30 kg/m(2) ). Median BMI at baseline was 27.6 kg/m(2) (interquartile range [IQR], 25.1-30.5 kg/m(2) ). Increased SAT and VAT were associated with a higher National Comprehensive Cancer Network (NCCN) PC risk group (P = .0001 and .008, respectively). Greater SAT was associated with a higher Gleason score (GS) (P = .030). Younger age at diagnosis was significantly correlated with higher SAT and BMI, whereas increased prostate size was found in patients with higher BMI, WC, SAT, and VAT. At a median follow-up of 42.3 months (IQR, 32.3-59.9 months), 15 secondary malignancies were observed. On multivariate analysis, VAT was a significant predictor for secondary cancers (adjusted hazards ratio, 1.014; P = .0001). CONCLUSIONS: Measurements of greater abdominal adiposity were strongly associated with adverse pathologic features in patients with localized PC, including higher GS and NCCN PC risk groups. Moreover, VAT was found to be a strong risk factor for secondary malignancies.
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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 | 0.002 |
| 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 | 0.001 |
| 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".