The effect of dutasteride on the detection of prostate cancer: A set of meta-analyses
Bibliographic record
Abstract
BACKGROUND: Dutasteride has been shown to significantly improve symptoms of benign prostatic hyperplasia (BPH) and reduce clinical progression. Recent data from studies evaluating 5-alpha reductase inhibitors (5-ARIs) for the prevention of prostate cancer, however, suggest 5ARIs, including dutasteride, may be associated with increased incidence of Gleason 8-10 prostate tumours. This meta-analysis was undertaken to quantify the effect of dutasteride on detection of prostate cancer and high-grade prostate cancer. METHODS: Our meta-analysis includes data from GlaxoSmithKline-sponsored phase III randomized clinical trials (with a study duration of ≥2 years) evaluating the effect of dutasteride, alone or in combination with tamsulosin, to treat BPH or to reduce the risk of prostate cancer. The incidence of prostate cancer, including Gleason 7-10 and Gleason 8-10, for patients taking either dutasteride, dutasteride plus tamsulosin, tamsulosin alone, or placebo, were evaluated using the Mantel-Haenszel Risk Ratio (MHRR) method of conducting meta-analyses. RESULTS: The meta-analysis demonstrated that in a population with symptomatic BPH and/or at increased risk of prostate cancer, a statistically significant lower number of detectable prostate cancers was found in men taking dutasteride compared to control groups (MHRR: 0.66, 95% CI 0.52-0.85). In our analysis, there was no increased risk for Gleason 7-10 (MHRR: 0.83, 95% CI 0.56-1.21) or Gleason 8-10 prostate cancers (MHRR: 0.99, 95% CI 0.39-2.53) in men taking dutasteride over control groups. There were several limitations that need to be considered when interpreting these results. CONCLUSION: These data provide support for the continued use of dutasteride in the treatment of symptomatic BPH patients.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.119 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".