Benign prostatic hyperplasia: epidemiology, economics and evaluation.
Bibliographic record
Abstract
INTRODUCTION: Benign prostatic hyperplasia (BPH) is arguably the most common benign disease of mankind. As men age, the prostate inexorably grows often causing troubling symptoms causing them to seek out care. While traditionally treated by transurethral resection or open surgical removal of the hypertrophied adenoma, today the urologist has numerous medical, surgical and minimally invasive techniques available. In this supplement The Canadian Journal of Urology provides a review of the various techniques and medications available today. MATERIALS AND METHODS: As an introduction to the supplement, the aim of this article is to review the epidemiology and economy of BPH as well as its natural history and diagnosis. A systematic review of available literature was looking for articles on BPH and its epidemiology, economics, natural history and management using PubMed database. RESULTS: The prevalence of this condition is increasing with the population aging and so does the economic burden. The exact etiology of this condition is unknown, but some risk factors have been identified. The diagnostic and treatment of this very common disease should rely on a strong collaboration between primary care physician and urologist. CONCLUSION: There are multiple options in treating BPH including medical, surgical and newer minimally invasive options. The challenge with having a variety of options is to review them with the patient and help the patient select the best treatment option for their condition.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".