Chronic Baseline Prostate Inflammation is Associated with Lower Tumor Grade in Men with Prostate Cancer on Repeat Biopsy: Results from the REDUCE Study
Bibliographic record
Abstract
PURPOSE: We investigated whether baseline acute or chronic prostate inflammation among men with initial negative biopsies for prostate cancer was associated with cancer grade in 2-year repeat biopsies. MATERIALS AND METHODS: Retrospective analyses were conducted of 889 men aged 50 to 75 years old with negative baseline prostate biopsy and 2-year repeat biopsy positive for prostate cancer in the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) study. Acute and chronic prostate inflammation and cancer grade were determined by central pathology during the REDUCE study. The association of inflammation in baseline and 2-year repeat biopsy and prostate cancer grade in 2-year repeat biopsy was evaluated with Student's t-test, chi-squared test and multivariable logistic regression. RESULTS: Chronic, acute inflammation and both were detected in 533 (60%), 12 (1%) and 85 (10%) baseline biopsies, respectively. Presence of acute and chronic inflammation were significantly associated with each other (p <0.001). Both types of inflammation were unrelated to race, body mass index, prostate specific antigen or digital rectal exam. At the 2-year biopsy, 621 (70%) tumors were low grade (Gleason scores 2-6) and 268 (30%) were high grade (Gleason scores 7-10). In univariable and multivariable analyses, men with baseline chronic inflammation had significantly fewer high grade tumors (univariable OR 0.64, 95% CI 0.47-0.87, p=0.004; multivariable OR=0.68, 95% CI0.50-0.93, p=0.016) than those without baseline chronic inflammation. Baseline acute inflammation was not associated with tumor grade (univariable OR 0.74, 95% CI 0.45-1.20, p=0.22; multivariable OR 0.78, 95% CI 0.48-1.29, p=0.34). CONCLUSIONS: Chronic inflammation in a negative biopsy was associated with lower prostate cancer grade among men with cancer on follow-up 2-year biopsy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".