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Record W4391544861 · doi:10.1016/j.cont.2024.101211

The outcomes of women undergoing an incision or excision of midurethral sling mesh

2024· article· en· W4391544861 on OpenAlexaff
Nada Ibrahim, Feras Allagany, Blayne Welk

Bibliographic record

VenueContinence · 2024
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsSling (weapon)MedicineSurgery

Abstract

fetched live from OpenAlex

Midurethral slings (MUS) are a common treatment for stress urinary incontinence, however in some cases complications require repeat surgery. Our objective was to assess the impact of MUS revision/removal procedures on subjective outcomes and use the validated Urogenital Distress Index questionnaire (UDI-6) to assess longer-term outcomes. We conducted a retrospective review of 48 patients who underwent MUS revision/removal between September 2013 and December 2021. Patients were categorized into three groups: Transection (TVSI), Excision (TVSE), and Complete excision (CSE). UDI-6 scores were collected at 3–6 months post-surgery. We found that de novo stress urinary incontinence occurred in 29%–40% of patients based on the type of mesh revision surgery. Worsening SUI was observed in 19% of TVSE and 40% of CSE patients. De novo urge incontinence was exclusive to TVSE (33%). UDI-6 scores after a median of 2–3 years indicated 25%–38% of patients achieved “asymptomatic” urinary health status, with most of the other women reporting mild to moderate symptoms. Questions on frequent urination and urge incontinence had the highest scores, while voiding dysfunction and pain had the lowest across the three types of MUS revision/removal. These results may help clinicians and patients make informed decisions regarding the treatment of MUS complications.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.323
Teacher spread0.301 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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