Continued 5α-Reductase Inhibitor Use after Prostate Cancer Diagnosis and the Risk of Reclassification and Adverse Pathological Outcomes in the PASS
Bibliographic record
Abstract
PURPOSE: Outcomes in patients who enroll in active surveillance programs for prostate cancer while receiving 5α-reductase inhibitors have not been well defined. We sought to determine the association of 5α-reductase inhibitor use with the risk of reclassification in the PASS (Canary Prostate Active Surveillance Study). MATERIALS AND METHODS: Participants in the multicenter PASS were enrolled between 2008 and 2016. Study inclusion criteria were current or never 5α-reductase inhibitors use, Gleason score 3 + 4 or less prostate cancer at diagnosis, less than a 34% core involvement ratio at diagnosis and 1 or more surveillance biopsies. Included in study were 1,009 men, including 107 on 5α-reductase inhibitors and 902 who had never received 5α-reductase inhibitors. Reclassification was defined as increase in the Gleason score and/or an increase to 34% or greater in the ratio of biopsy cores positive for cancer. Adverse pathology at prostatectomy was defined as Gleason 4 + 3 or greater and/or nonorgan confined disease (pT3 or N1). RESULTS: On multivariable analysis there was no difference in reclassification between men who had received and those who had never received 5α-reductase inhibitors (HR 0.81, p = 0.31). Patients who had received 5α-reductase inhibitors were less likely to undergo radical prostatectomy (8% vs 18%, p = 0.01) or any definitive treatment (19% vs 24%, p = 0.04). In the 167 participants who underwent radical prostatectomy there was no suggestion of a difference in the rate of adverse pathology findings at prostatectomy between 5α-reductase inhibitor users and nonusers. CONCLUSIONS: Continued 5α-reductase inhibitor use after an initial diagnosis of prostate cancer was not associated with the risk of reclassification on active surveillance in men in the PASS cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".