Association between body mass index and localized prostate cancer management and disease‐specific quality of life
Bibliographic record
Abstract
Abstract Purpose The purpose of this work is to describe the association between body mass index (BMI) and (1) management option for localized prostate cancer (PCa) and (2) disease‐specific quality of life (ds‐QoL) after treatment or active surveillance. Subjects/patients and methods We analysed data from men with localized PCa managed with radical prostatectomy (RP), radiation therapy (RT), or active surveillance (AS) in a prospective, population‐based cohort study. We evaluated the association between BMI and management option with multivariable multinomial logistic regression analysis. The association between BMI and ds‐QoL was assessed using multivariable longitudinal linear regression. Regression models were adjusted for baseline domain scores, demographics, and clinicopathologic characteristics. Results A total of 2378 men were included (medians [quartiles]: age 64 [59–69] years; BMI 27 kg/m2; 77% were non‐Hispanic white); 29% were obese (BMI ≥ 30). Accounting for demographic and clinicopathologic features, BMI ≥ 28 kg/m2 was inversely associated with the likelihood of receiving RP (compared with RT) and became statistically significant at BMI ≥ 33 kg/m2 (maximum adjusted relative risk ratio = 0.80, 95% CI 0.67 to 0.95, p = 0.013 for BMI ≥ 33 vs. 25). Conversely, BMI was not significantly associated with the likelihood of receiving AS compared with RT. After stratification by management option, obese men who underwent definitive treatment were not found to have clinically worse ds‐QoL. Obese men initially on AS appeared to have worse urinary incontinence than nonobese men, but this was not significant on an as‐treated sensitivity analysis. Conclusions Among men with localized PCa, those with BMI ≥ 33 kg/m2 were less likely to receive surgery than radiation. Obesity was not associated with ds‐QoL in men undergoing definitive treatment, nor in men who remained on AS.
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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 | 0.003 |
| 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".