Body Mass Index and its Association with Genitourinary Disorders in Men Undergoing Prostate Cancer Screening
Bibliographic record
Abstract
INTRODUCTION: Elevated body mass index (BMI) may predispose to several pelvic pathologies. AIMS: We tested the association between BMI and five end points, namely, (i) erectile dysfunction (ED); (ii) lower urinary tract symptoms (LUTS); (iii) chronic prostatitis-associated pain (CPP); and ejaculatory dysfunction that is subdivided between (iv) pain/discomfort on ejaculation; and (v) subjectively decreased ejaculate volume. METHODS: Age, height, and weight were prospectively recorded in a cohort of 590 consecutive healthy men undergoing prostate cancer screening. Continuously coded and categorized BMI (World Health Organization classification) were studied. MAIN OUTCOME MEASURES: Age-adjusted analyses relied on logistic and linear regression models, according to data type. RESULTS: The average age was 54.1 years (range 30-83). Of all, 296 were overweight (50.2%, BMI 25-29.9 kg/m(2)) and 85 were obese (14.4%, BMI > or = 30 kg/m(2)). After age adjustment, elevated continuously coded BMI (P < 0.001) and elevated categorized BMI (P = 0.01) were associated with worse erectile function. Conversely, after age adjustment, elevated continuously coded BMI (P = 0.02) and elevated categorized BMI (P = 0.05) were associated with a lower rate of subjectively decreased ejaculate volume. Finally, after age adjustment, elevated categorically coded BMI was related to lower rates of CPP (P < 0.001) and to a lower rate of pain/discomfort on ejaculation (P = 0.03). CONCLUSIONS: In men undergoing prostate cancer screening, the effect of BMI on the five end points is not invariably detrimental. Elevated BMI may predispose to ED, but may also decrease the rate of pain/discomfort on ejaculation and may lower the reported rate of subjectively decreased ejaculate volume. Finally, it appeared to have no effect on LUTS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".