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Record W2604317616 · doi:10.1016/j.juro.2017.02.453

MP13-10 SURGICAL MANAGEMENT OF BENIGN PROSTATIC OBSTRUCTION: 20-YEAR POPULATION-LEVEL TRENDS

2017· article· en· W2604317616 on OpenAlexaboutno aff
Christopher Wallis, Lesley K. Carr, Sender Herschorn, Refik Saskin, Sidney B. Radomski, Armando Lorenzo, Robert K. Nam

Bibliographic record

VenueThe Journal of Urology · 2017
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHyperplasiaGeneral surgeryProstatectomyPopulationProstateOpen ProstatectomyCancerInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyBenign Prostatic Hyperplasia: Epidemiology & Evaluation1 Apr 2017MP13-10 SURGICAL MANAGEMENT OF BENIGN PROSTATIC OBSTRUCTION: 20-YEAR POPULATION-LEVEL TRENDS Christopher Wallis, Lesley Carr, Sender Herschorn, Refik Saskin, Sidney Radomski, Armando Lorenzo, and Robert Nam Christopher WallisChristopher Wallis More articles by this author , Lesley CarrLesley Carr More articles by this author , Sender HerschornSender Herschorn More articles by this author , Refik SaskinRefik Saskin More articles by this author , Sidney RadomskiSidney Radomski More articles by this author , Armando LorenzoArmando Lorenzo More articles by this author , and Robert NamRobert Nam More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.453AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Benign prostatic obstruction (BPO) due to histologic benign prostatic hyperplasia is highly prevalent among older men. Despite widespread use of medical therapy, surgical treatment remains a mainstay in the management of BPO. We sought to characterise trends in the surgical management of BPO in a single-payer healthcare system in Ontario, Canada over a 20 year period. METHODS We performed an interrupted time-series analysis using segmented regression among men aged 18 years and older undergoing surgical treatment for BPO between January 1, 1994 and December 31, 2014 in Ontario, Canada. The passage of time was considered the primary exposure. The primary outcome was the proportion of all BPO surgeries performed using each of the following modalities: transurethral resection of the prostate (TURP), endoscopic laser prostatectomy, open/laparoscopic prostatectomy, and others. Secondary outcomes included trends in the age and comorbidity of patients undergoing BPO surgery. RESULTS We identified 136,459 men who underwent BPO surgery between 1994 and 2014. Across the study interval, the annual age-adjusted rate of BPO surgery declined significantly (24 per 10,000 population in 1995 to 10 per 10,000 population in 2014). We identified two distinct epochs with respect to treatment modality. From 1994 to 2001, there were no significant changes in the distribution of BPO surgical modalities with TURP the most common throughout (97.2% in 1994 and 97.0% in 2001). In the period 2002 to 2014, there was a significant decline in the use of TURP (92.1% to 76.9%; p=0.027) with a corresponding increase in the use of endoscopic laser prostatectomy (3.5% to 21.9%; p=0.0008). We identified small but statistically significant increases in the age (p=0.0004) and comorbidity (p<0.0001) of patients undergoing BPO surgery over time. CONCLUSIONS This large, population-based study demonstrates a shift in the management of BPO with increasing use of endoscopic laser prostatectomy, beginning in 2002. However, TURP remains the most common treatment modality. We also identified shifting demographics of patients undergoing BPO surgery with a trend for patients to be older and have greater comorbid disease at the time of surgery in more recent years. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e157 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Christopher Wallis More articles by this author Lesley Carr More articles by this author Sender Herschorn More articles by this author Refik Saskin More articles by this author Sidney Radomski More articles by this author Armando Lorenzo More articles by this author Robert Nam More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.060
GPT teacher head0.359
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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