Does 5-alpha Reductase Inhibitor Use Improve The Efficacy of Intravesical Bacille Calmette-Guérin (BCG) for Non-muscle Invasive Bladder Cancer?
Bibliographic record
Abstract
BACKGROUND: Prior research implicates the androgen receptor pathway as important in bladder cancer progression and recurrence. In particular, use of 5-alpha reductase inhibitors (5-ARIs) appears to improve bladder cancer outcomes. This study aims to determine if concomitant use of 5-ARIs with intravesical Bacille Calmette-Guérin (BCG) decreases recurrences in patients with non-muscle invasive bladder cancer (NMIBC). MATERIALS & METHODS: This retrospective analysis included male patients diagnosed with NMIBC who were treated with induction intravesical BCG at our institution from 2013 to 2018. Patients were excluded who received prior induction BCG. Recurrence and progression-free survival were estimated using the Kaplan-Meier method. Univariate and multivariate analyses were performed using Cox proportional hazards and Poisson events models. RESULTS: We identified 206 male patients, of whom 39 received 5-ARIs and 167 did not. Most patients (72.8%) received >6 instillations of BCG, with 20.4% completing all induction and maintenance treatments during a median follow up of 3.31 years. There were no significant differences in recurrence or progression-free survival between 5-ARI users or non-users (59% vs 55.7%; p = 0.72 and 97.4% vs 98.2%; p = 1.00, respectively). Similarly, no differences in the number of recurrences between groups was observed ( p = 0.78). However, the proportion of patients who completed all prescribed BCG installations was higher among 5-ARI users (39.7% vs 17.9 %, p = 0.07). CONCLUSIONS: In contrast to prior reports, our study does not suggest that 5-ARI use decreases the recurrence rate in men receiving induction BCG for NMIBC. However, our results suggest 5-ARI use may improve patient tolerance to BCG.
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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) | 0.001 | 0.001 |
| 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 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".