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Record W1973451816 · doi:10.1002/nau.20764

Executive Summary: The International Consultation on Incontinence 2008—Committee on: “Dynamic Testing”; for urinary incontinence and for fecal incontinence. part 1: Innovations in Urodynamic Techniques and Urodynamic Testing for signs and symptoms of urinary incontinence in female patients

2009· review· en· W1973451816 on OpenAlexaff
Peter Rosier, Jerzy B. Gajewski, Peter K. Sand, László Szabó, Ann Capewell, Gordon Hosker

Bibliographic record

VenueNeurourology and Urodynamics · 2009
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineUrinary incontinenceUrodynamic testingUrodynamic studiesFecal incontinenceStress incontinenceGynecologyUrologyGeneral surgerySurgery

Abstract

fetched live from OpenAlex

AIMS: The members of The International Consultation on Incontinence 2008 (Paris) Committee on Dynamic Testing' provide an executive summary of the chapter 'Dynamic Testing' that discusses (urodynamic) testing methods for patients with signs and or symptoms of urinary incontinence. Testing of patients with signs and or symptoms of faecal incontinence is also discussed. METHODS: Evidence based and consensus committee report. RESULTS: The chapter 'Dynamic Testing' is a continuation of previous Consultation-reports added with a new systematic literature search and expert discussion. Conclusions, based on the published evidence and recommendations, based on the integration of evidence with expert experience and discussion are provided separately, for transparency. CONCLUSION: This first part of a series of three articles summarizes the committees recommendations about the innovations in urodynamic study techniques 'in general', about the test characteristics and normal values of urodynamic studies as well as the assessment of female with signs and or symptoms of incontinence and includes only the most recent and relevant literature references.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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.

Study designObservational
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

Citations28
Published2009
Admission routes1
Has abstractyes

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