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Record W4399364218 · doi:10.1002/nau.25445

Recommendations of the SUFU/AUGS/ICS Female Stress Urinary Incontinence Surgical Publication Working Group: A common standard minimum data set for the literature

2024· review· en· W4399364218 on OpenAlexaff
Eric Rovner, Christopher Chermansky, Elisabetta Costantini, Roger R. Dmochowski, Ekene Enemchukwu, David A. Ginsberg, John Heesakkers, Shawn A. Menefee, Geneviève Nadeau, Charles R. Rardin, Philippe E. Zimmern

Bibliographic record

VenueNeurourology and Urodynamics · 2024
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversité Laval
FundersAmerican Urogynecologic Society
KeywordsMedicineContext (archaeology)Urinary incontinenceDelphi methodStandardizationMinimum Data SetPhysical therapySurgeryNursingStatistics

Abstract

fetched live from OpenAlex

INTRODUCTION AND OBJECTIVES: Relevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal. The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. METHODS: The WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. RESULTS: The WG outlined standardization in four major areas: (1) study design, (2) pretreatment demographics and characterization of the study population, (3) intraoperative events, and (4) posttreatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD-must be included; ADDITIONAL-may be included for a specific study and is inclusive of the Standard items; OPTIMAL-may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY-not relevant. CONCLUSIONS: A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.065
GPT teacher head0.364
Teacher spread0.299 · 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 designNot applicable
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

Citations7
Published2024
Admission routes1
Has abstractyes

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