Knowledge and Use of Finasteride for the Prevention of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: The knowledge about and use of chemopreventive agents for prostate cancer by physicians has not been described. The Prostate Cancer Prevention Trial (PCPT) showed that finasteride was effective in reducing the incidence of prostate cancer. We examined the influence of the PCPT on finasteride prescribing within the Veterans Health Administration (VHA). METHODS: We assessed trends on monthly new and total prescriptions for finasteride filled within the VHA from January 2000 to December 2005. Additionally, all VHA urologists and a random sample of VHA primary care physicians (PCP) were surveyed about their use of finasteride. RESULTS: The number of men starting finasteride grew over the study period. Publication of the PCPT was not significantly associated with any change in this pattern (P = 0.45). Fifty-seven percent of urologists and 40% of PCPs endorsed prescribing finasteride more frequently in 2006 than 5 years prior. However, among those who reported changing prescribing patterns, fewer than 2% reported being influenced by the PCPT. Sixty-four percent of urologists and 80% of PCPs never prescribe finasteride for prostate cancer chemoprevention; 55% of urologists cited concerns of inducing high-grade tumors, whereas 52% of PCPs did not know it could be used for chemoprevention. CONCLUSIONS: The number of men starting finasteride in the VHA increased over time, but the change did not seem to be due to increased use of finasteride for chemoprevention. Publication of the PCPT seemed to have little influence over the study period. IMPACT: Physicians may not readily accept the use of chemopreventive agents for prostate cancer.
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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 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".