Does the digital rectal exam still provide value in the age of MRI?
Bibliographic record
Abstract
INTRODUCTION: Accurate staging of prostate cancer is essential for treatment planning and prognosis. While digital rectal exam (DRE) has traditionally been used, its limitations in detecting extracapsular extension (ECE) have led to increased reliance on multiparametric magnetic resonance imaging (mpMRI). METHODS: This study compared outcomes between T3 prostate cancer diagnosed by DRE vs. mpMRI only (i.e., not T3 by DRE) using data from the Alberta Prostate Cancer Research Initiative. The cohort included all 536 patients with cT3NxMx prostate cancer diagnosed between July 2014 and July 2024. The primary outcome was overall survival, with secondary outcomes including age at diagnosis, prostate-specific antigen (PSA) at diagnosis, treatment modality, Gleason grade group, and metastasis at diagnosis. RESULTS: Patients diagnosed as T3 by DRE were significantly older (71.6 vs. 67.9, p<0.001), had higher PSA levels (32% vs. 11% PSA >20 ng/ml, p<0.001), and higher Gleason grade groups (39% vs. 15% GG4+, p<0.001) compared to those diagnosed by mpMRI. DRE-diagnosed patients underwent radiation therapy and primary androgen deprivation therapy more frequently than MRI-diagnosed patients. DRE-diagnosed patients also had higher rates of metastases at diagnosis (16% vs. 5%, p<0.001) and worse overall survival (hazard ratio 4.6, 95% confidence interval 1.4-15.0, p=0.002). CONCLUSIONS: T3 prostate cancer diagnosed by DRE is associated with more advanced disease, higher metastasis rates, and worse survival compared to mpMRI-diagnosed T3 disease. These findings suggest that T3 disease identified by DRE represents a more aggressive cancer subtype and should be considered higher-risk in clinical decision-making.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".