Abdominal Adiposity and Testosterone Levels in Patients with Localized Prostate Cancer
Bibliographic record
Abstract
Objectives: To analyze the relationship between body mass index (BMI), fat tissue distribution, and total testosterone (TT) levels in men with localized prostate cancer (PCa). Methods: Two hundred and thirty-eight patients with either intermediate-risk PCa (60.5%) or high-risk PCa (39.5%) treated by external beam radiotherapy (EBRT), with or without androgen deprivation therapy (ADT), were analyzed. The visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were identified from patient’s radiotherapy planning computed tomography (CT) images. Patients were divided into two groups: the first included patients whose TT levels were hypogonadal; the second included patients whose TT levels were normal. Results: There was a correlation between VAT and BMI (r=0.627). In an univariate model, BMI (p = 0.0174) and VAT (p=0.0228) were both predictive for low TT; age (p=0.579) was not. In a multivariate analysis taking into account the non-linear relationship between a low TT and BMI, BMI was the only predictive factor of a low TT (p=0.0304). An increase in BMI from 15 to 35 kg/m2 was associated with a fairly linear increase in the probability of having a low TT level; an increase in BMI of 5 kg/m2 resulted in an increase in probability of a low TT of 10%. On the other hand, a BMI >35 kg/m2 had a lower probability of a low TT level. Conclusions: There is a non-linear relationship between hypogonadal TT levels and BMI. VAT was not an independent predictor for low TT levels. Our results emphasize the important relationship between low TT and obesity in patients with PCa.
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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".