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Record W3207143988 · doi:10.1097/mou.0000000000000937

Surgical treatment options for benign prostatic obstruction: beyond prostate volume

2021· article· en· W3207143988 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of TorontoCentre Hospitalier de l’Université de MontréalUniversity Health NetworkUniversité de Montréal
Fundersnot available
KeywordsProstateProstate cancerMEDLINEClinical PracticeVolume (thermodynamics)

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Many surgical treatment options are available for patients who present with benign prostatic obstruction (BPO). This article reviews the current treatment options available and distinguishes them based on five clinical considerations: antithrombotic therapy, sexual function preservation, ambulatory procedures, anesthesia-related risks and duration of catheterization. RECENT FINDINGS: A comprehensive review of the literature was performed on 10 BPO procedures. Laser enucleation of the prostate (LEP), bipolar plasma transurethral vaporization of the prostate and photoselective vaporization (PVP) of the prostate reduces the risk of bleeding, which is recommended for anticoagulated men. Ejaculatory function is more likely to be preserved following transurethral incision of the prostate, Rezūm, Aquablation, UroLift and iTind. Same-day discharge is possible for LEP, PVP and prostatic arterial embolization (PAE). For patients with high anesthesia-related risks, procedures compatible with local anesthesia (UroLift, Rezūm, iTind and PAE) should be favored. Catheterization duration is shorter with UroLift, PVP and LEP. SUMMARY: BPO treatment options are growing rapidly. The optimal procedure for a given patient is based on factors such as associated risks, recovery and expected outcomes. Besides prostate volume, the clinical considerations in the present article can help elucidate the best surgical BPO treatment option for each patient based on their values, preferences, and risk tolerance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

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

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 head0.080
GPT teacher head0.399
Teacher spread0.319 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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