Canadian trend in surgical management of benign prostatic hyperplasia (BPH) and laser therapy from 2007-2008 to 2011-2012
Bibliographic record
Abstract
INTRODUCTION: Clinically benign prostatic hyperplasia (BPH) is classically associated by the progressive development of lower urinary tract symptoms (LUTS). The incidence of bothersome LUTS is associated with age and may vary in patients over 50 years old. In many developing countries with an aging population, BPH associated with LUTS has become a major health issue. To optimize quality of care and control of cost, there is an imperative need to examine the pattern of BPH management. The goal of this study is to capture the Canadian trend in surgical management of BPH and the use of laser therapy during the last 5 years from 2007-2008 to 2011-2012. METHODS: We collected the number of transurethral resection of the prostate (TURP) procedures performed in each province in Canada from the Canadian Institute for Health Information (CIHI) reports for the fiscal years (April 1(st)-March 31(st)) of 2007-2008, 2008-2009, 2009-2010, 2010-2011 and 2011-2012. RESULTS: Overall, the total number of TURP procedures remained stable from 20 294 procedures per year in 2007 to 20 629 in 2011. In terms of distribution according to provinces, in 2011, about 40% of procedures were performed in Ontario, 20% in BC, 18 in Quebec and 8% in Alberta. These proportions between provinces have remained similar and stable between 2007 and 2011. In contrast, the number of alternative minimally invasive procedures has slowly grown from 767 interventions in 2007 to 1559 in 2011. Overall, laser procedures represented 7.6% of the total number of BPH surgeries in Canada in 2011. The contribution of laser therapy to the amount of total BPH procedures largely varied between provinces. CONCLUSIONS: The use of minimally invasive laser procedure alternatives to TURP is progressively growing. Among the novel laser therapies, HoLEP and GreenLight vaporization are the only procedures that have demonstrated equivalent outcomes compared to TURP in randomized clinical trials. Furthermore, due to shorter hospital stay, these novel laser modalities have the potential to reduce healthcare expenses for the treatment of BPH. We can infer that following the trend observed in the United States, the number of laser therapies for BPH in Canada may increase significantly during the coming years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".