Utility of digital rectal examination in a population with prostate cancer treated with active surveillance
Bibliographic record
Abstract
INTRODUCTION: Digital rectal examination (DRE) is part of the clinical evaluation of men on active surveillance (AS). The purpose of the present study is to analyze the value of DRE as a predictor of upgrading in a population of men with prostate cancer (PCa) treated with AS. METHODS: We used the prostate biopsy (PBx) database from an academic center, including PBx from 2006-2018, and identified 2029 confirmatory biopsies (CxPBx) of men treated with AS, of which 726 men had both diagnostic (initial) and CxPBx information available. We did a descriptive analysis and evaluated sensitivity, specificity, and predictive values of DRE for the detection of clinically significant PCa (csPCa). Multivariable regression analysis was done to identify predictors of csPCa. The primary outcome was to evaluate DRE as a predictor of the presence of csPCa at CxPBx. RESULTS: Among the 2029 patients with a CxPBx, 75% had PCa, and of these, 30.3% had upgrading to International Society of Urologic Pathologists (ISUP) grade ≥2. Thirteen percent of men had a suspicious DRE (done by their treating physician). Sensitivity, specificity, negative and positive predictive values of DRE to detect csPCa were best with a prostate-specific antigen (PSA) <4 ng/ml (27%, 88%, 31%, and 87%, respectively). A suspicious DRE at CxPBx, particularly if the DRE at diagnosis was negative, was a predictor of csPCa (odds ratio [OR] 2.34, p=0.038). The main limitation of our study is the retrospective design and the lack of magnetic resonance imaging. CONCLUSIONS: We believe DRE should still be used as part of AS and can predict the presence of csPCa, even with low PSA values. A suspicious nodule on DRE represents a higher risk of upgrading and should prompt further assessment.
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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 | 0.004 |
| 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 | 0.001 | 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".