Obesity does not predispose to more aggressive prostate cancer either at biopsy or radical prostatectomy in European men
Bibliographic record
Abstract
Many investigators suggested that obesity predisposes to adverse prostate cancer characteristics and outcomes. We tested the effect of obesity on the rate of aggressive prostate cancer at either prostate biopsy or radical prostatectomy (RP). Clinical and pathological data were available for 1,814 men. Univariable and multivariable logistic regression models addressed the rate of high grade prostate cancer (HGPCa) at either biopsy or final pathology. Clinical stage, prostate-specific antigen (PSA), percentage of free PSA and prostate volume were the base predictors. All models were fitted with and without body mass index (BMI), which quantified obesity. BMI and its reciprocal (InvBMI) were coded as cubic splines to allow nonlinear effects. Predictive accuracy (PA) was quantified with area under curve estimates, which were subjected to 200 bootstrap re-samples to reduce overfit bias. Gains in PA related to the inclusion of BMI were compared using the Mantel-Haenszel test. HGPCa at biopsy was detected in 562 (31%) and HGPCa at RP pathology was present in 931 (51.3%) men. In either univariable or multivariable models predicting HGPCa at biopsy, BMI or InvBMI failed to respectively reach statistical significance or add to multivariable PA (BMI gain = 0%, p = 1.0; InvBMI gain = -0.2%, p = 0.9). Conversely, in models predicting HGPCa at RP, BMI and InvBMI represented independent predictors but failed to increase PA (BMI gain = 0.7%, p = 0.6; InvBMI gain = 0.5, p = 0.7%). Obesity does not predispose to more aggressive prostate cancer at biopsy. Similarly, obesity does not change the ability to identify those who may harbor HGPCa at RP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".