Recommendations of the SUFU/AUGS/ICS Female Stress Urinary Incontinence Surgical Publication Working Group: A Common Standard Minimum Data Set for the Literature
Bibliographic record
Abstract
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) post-treatment 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.421 | 0.623 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.054 | 0.027 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.027 | 0.022 |
| Research integrity | 0.034 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".