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Record W2625398332 · doi:10.5489/cuaj.4610

Management of patients with stress urinary incontinence after failed midurethral sling

2017· review· en· W2625398332 on OpenAlexaffvenue
Alex Kavanagh, May Sanaee, Kevin Carlson, G Bailly

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsDalhousie UniversityUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsSling (weapon)MedicineUrinary incontinenceSurgeryStress incontinenceNeck of urinary bladderConservative managementArtificial urinary sphincterPessaryUrinary bladder

Abstract

fetched live from OpenAlex

Surgical failure rates after midurethral sling (MUS) procedures are variable and range from approximately 8‒57% at five years of followup. The disparity in long-term failure rates is explained by a lack of long-term followup and lack of a clear definition of what constitutes failure. A recent Cochrane review illustrates that no high-quality data exists to recommend or refute any of the different management strategies for recurrent or persistent stress urinary incontinence (SUI) after failed MUS surgery. Clinical evaluation requires a complete history, physical examination, and establishment of patient goals. Conservative treatment measures include pelvic floor physiotherapy, incontinence pessary dish, commercially available devices (Uresta®, Impressa®), or medical therapy. Minimally invasive therapies include periurethral bulking agents (bladder neck injections) and sling plication. Surgical options include repeat MUS with or without mesh removal, salvage autologous fascial sling or Burch colposuspension, or salvage artificial urinary sphincter insertion. In this paper, we present the available evidence to support each of these approaches and include the management strategy used by our review panel for patients that present with SUI after failed midurethral sling.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.372
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.275
Teacher spread0.254 · 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 designObservational
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

Citations20
Published2017
Admission routes2
Has abstractyes

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