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Record W2124273443

Photoselective vaporization for the treatment of benign prostatic hyperplasia.

2013· article· en· W2124273443 on OpenAlexaffabout
James M. Bowen, J. Paul Whelan, Robert Hopkins, Natasha Burke, Edward A. Woods, Gary P McIsaac, Daria O’Reilly, Feng Xie, S Sehatzadeh, Leslie Levin, Suja P. Mathew, Lisa L. Patterson, Ron Goeree, Jean‐Éric Tarride

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonPrograms for Assessment of Technology in Health Research Institute
Fundersnot available
KeywordsMedicineLower urinary tract symptomsInternational Prostate Symptom ScoreProstateUrologyQuality of life (healthcare)Transurethral resection of the prostateGynecologySurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: As an alternative to transurethral resection of the prostate (TURP), photoselective vaporization of the prostate (PVP) provides a bloodless, relatively painless relief of lower urinary tract symptoms for men with benign prostatic hyperplasia. Following a review of the evidence in 2006, the Ontario Health Technology Advisory Committee recommended that a study be conducted to evaluate PVP in Ontario. OBJECTIVES: To compare the clinical effectiveness, safety, cost-effectiveness, and budget impact of PVP compared to conventional TURP for the treatment of benign prostatic hyperplasia in Ontario. METHODS: A prospective, nonrandomized trial was conducted in 3 Ontario centres. Consenting subjects were assessed at baseline and 1, 3, and 6 months following surgery. Outcome measures included International Prostate Symptom Score (IPSS), peak urinary flow rate (Qmax), post-void residual (PVR) volume, prostate-specific antigen (PSA), health-related quality of life (HRQOL) using the EuroQol 5 Domain questionnaire, and the Sexual Health Inventory for Men (SHIM) score. Adverse events, resource utilization, and productivity losses were also assessed. Cost-effectiveness and budget impact analyses were completed using data from the study. RESULTS: Between February 2008 and August 2010, 164 subjects were enrolled in the study (n = 140 for PVP and n = 24 for TURP). Treatment outcomes were similar between the 2 groups at 6 months, with the IPSS decreasing similarly over time (P = 0.718). For other treatment outcomes (Qmax, PSA, HRQOL, SHIM) both treatments provided similar benefit over time; only changes in PVR volume favoured PVP (P = 0.018). The majority of PVP patients were managed on an outpatient basis, with only 7.1% requiring admission (all TURP subjects were inpatients). At 6 months, PVP was less costly than TURP ($3,891 versus $4,863; P = 0.001), with similar quality-adjusted life-years (0.448 versus 0.441; P = 0.658). PVP remained the most cost-effective treatment across all decision-making thresholds, with the technology costing less and providing similar clinical outcomes. Extrapolating the results to a provincial level indicated (based on an estimated case volume of 12,335 TURPs) that there is an opportunity to reallocate just over $14 million (Cdn), primarily related to the reduced need for hospital admission. LIMITATIONS: This study was nonrandomized, and the results should be interpreted with some caution, despite generally similar baseline characteristics between the 2 groups. Recruiting individuals to the TURP arm was a challenge, resulting in a size imbalance between treatment arms. CONCLUSIONS: Based on this analysis, PVP appears to be a cost-effective alternative to TURP, providing similar clinical benefit at a lower cost to the health system.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.040
GPT teacher head0.280
Teacher spread0.240 · 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 designBench or experimental
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".

Quick stats

Citations11
Published2013
Admission routes2
Has abstractyes

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