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Record W4410881567 · doi:10.1016/j.cont.2025.101906

Group-based pelvic floor muscle training for urinary incontinence in Postmenopausal Women: Tips and tricks for successful practice: ICS 2023 workshop

2025· article· en· W4410881567 on OpenAlexafffundabout
Gabrielle Carrier-Noreau, Mélanie Le Berre, Joanie Mercier, S Mont-Briant, Chantale Dumoulin

Bibliographic record

VenueContinence · 2025
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health ResearchRéseau québécois de recherche sur le vieillissement
KeywordsPelvic Floor MuscleUrinary incontinenceMedicinePostmenopausal womenPhysical therapyUrologyPelvic floorPhysical medicine and rehabilitationInternal medicineAnatomy

Abstract

fetched live from OpenAlex

Pelvic floor muscle training (PFMT) is the recommended first-line treatment for urinary incontinence and lower urinary tract symptoms. However, global availability of human and financial resources limits its accessibility. Group-based PFMT, which has been studied in postmenopausal women, offers a potential solution. This paper is based on the Group-based intervention for pelvic floor muscle dysfunctions in postmenopausal women workshop held during the 2023 International Continence Society Annual Scientific Meeting, in Toronto. It reviews the current evidence on group-based PFMT, discusses participant inclusion criteria, details the structure of the 12-week PFMT programme, presents remote group-based PFMT as an alternative to in-person group-based PFMT and proposes tips and tricks to empower clinicians in conducting Group-Based interventions for urinary incontinence and lower urinary tract symptoms.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.021
GPT teacher head0.320
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2025
Admission routes3
Has abstractyes

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