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Behavioral Therapies for Overactive Bladder

2008· review· en· W2330724082 on OpenAlexaff
Jill Milne

Bibliographic record

VenueJournal of Wound Ostomy and Continence Nursing · 2008
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsOveractive bladderMedicinePelvic Floor MusclePelvic floorUrinary incontinenceNeuromodulationPhysical therapyRehabilitationLifestyle modificationBehavioral therapyQuality of life (healthcare)Physical medicine and rehabilitationIntensive care medicineUrologyInternal medicineObesitySurgeryAlternative medicineNursingPathology

Abstract

fetched live from OpenAlex

Overactive bladder (OAB) is a symptom-based syndrome characterized by the presence of urgency, which is defined as a sudden and compelling desire to void that cannot be postponed. OAB may significantly impact of quality of life. Numerous treatment options exist for OAB, including behavioral therapies such as pelvic floor muscle rehabilitation, bladder training, and dietary modification, as well as traditional therapies such as pharmacological therapy and neuromodulation. Behavioral therapies are considered the mainstay of treatment for urinary incontinence in general. However the efficacy of these noninvasive strategies for OAB treatment has not been well addressed in the literature. This article presents an overview of current evidence with attention to the clinical relevance of findings related to lifestyle modification, bladder training, and pelvic floor muscle training. Initial evidence suggests that obesity, smoking, and consumption of carbonated drinks are risk factors for OAB but there is less support for the contributory role of caffeine or the impact of caffeine reduction. The evidence supporting bladder training and pelvic floor muscle training is more consistent and a trend towards combining these therapies to treat OAB appears positive. Given the prevalence of OAB and growing support for the efficacy of behavioral treatments it is important and timely to augment existing evidence with well-designed multicenter trials.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.057
GPT teacher head0.393
Teacher spread0.336 · 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 designOther design
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

Citations52
Published2008
Admission routes1
Has abstractyes

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