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Comparison of Two Treatment Modalities in Overactive Bladder: a Rapid Overview

2022· review· en· W4312449330 on OpenAlexaff
Abdulrahim A. Mirza, Mohammed A Alamoudi, Ahmad A. Mirza, Munirah Alsubaie, Mohammed Abuzenada

Bibliographic record

VenueMedical Archives · 2022
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOveractive bladderMedicineNocturiaSacral nerve stimulationUrologyBotulinum toxinUrinary systemSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Overactive bladder is a syndrome of urgency with the absence of infection and is usually accompanied by frequency and nocturia. Both sacral neuromodulation (SNM) and intravesical botulinum toxin (BTX) injection are used contentiously in overactive bladder after failure of conservative management. Objective: We aimed to provide an updated comparative overview of BTX injection versus SNM in the treatment of overactive bladder. Methods: PubMed and Google scholar databases were explored starting from 2016 to 2022 to obtain relevant articles. English studies on adult population were included. Results: Six articles included 648 participants were included in the current overview. Both BTX and SNM are comparable for the treatment of overactive bladder, with no major complications were reported. However, the use of SNM for refractory overactive bladder after failure of BTX was more successful and effective and resulted in patient satisfaction compared to the use of BTX after failure of SNM. Conclusion: Our update provides the latest comparative overview of outcomes of BTX versus SNM in the treatment of overactive bladder.

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)0.003
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0090.007
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.259
GPT teacher head0.514
Teacher spread0.255 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2022
Admission routes1
Has abstractyes

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