Bibliographic record
Abstract
Introduction and Objective: Benign prostatic hyperplasia (BPH) is one of the most common urological complaints encountered by general practitioners (GPs).5-α-reductase inhibitors (5-ARI) are new synthetic agents currently used in its management.We sought to investigate practice patterns, indications for urological consultations and level of awareness by GPs who treat patients with 5-ARIs. Materials and Methods:We conducted a survey which was mailed in both French and English to over 8000 registered GPs in Quebec.The questions covered each physician's BPH patient load, their preferred management plans for BPH, their knowledge of and comfort with 5-ARI, the type of 5-ARI they most often prescribed and clinical scenarios related to their use, when to refer to a urologist, and their role in prostate cancer chemoprevention.Results: Of the surveys mailed, 520 (6.5% response rate) were returned.Mean years from graduation was 24.8 (median 25, range 3-58) years with more than 90% of responders indicating they see less than 20 patients per week with BPH and 1-5 new consults for lower urinary tract symptoms.Overall, 34.1% of GPs initiated 5-ARI therapy themselves while the rest were renewals initiated by the urologist.Among GPs, 20% and 12% use 5-ARI alone and in combination as first-line therapy for BPH.Once on therapy, 73% do not refer to urology if PSA does not decline after 6-12 months of 5-ARI treatment.Only 7% correlated 5-ARI with more aggressive prostate cancer.Interestingly, 39.1% would not advocate its use in chemoprevention for prostate cancer in asymptomatic patients regardless of the percent risk reduction.Conclusion: There still is a preference among GPs to use α-blockers over 5-ARI in the management of BPH as well as a hesitancy to use them as preventative measures for prostate cancer.Importantly, there is a lack of awareness of the effect of 5-ARIs on PSA kinetics and reluctance to refer to a urologist in the face of stable borderline PSA despite 5-ARI therapy.Further education among GPs needs to be instilled about these drugs in order to safely optimize their usage at the primary care level and to avoid delays in cancer detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.559 | 0.250 |
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".