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MP06-11 DEVELOPMENT OF A NEW CANADIAN ENDOUROLOGY GROUP STENT SYMPTOM SCORE (CEGSSS)

2021· article· en· W3191848263 on OpenAlexaboutno aff
Naeem Bhojani, Jason Y. Lee, Shubhadip K. De, Andrea G. Lantz, Sri Sivalingam, Michael Ordon, Kymora B. Scotland, Sero Andonian, Ben H. Chew

Bibliographic record

VenueThe Journal of Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStentLikert scaleClinical PracticeFamily medicineSurgery

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyStone Disease: Surgical Therapy I (MP06)1 Sep 2021MP06-11 DEVELOPMENT OF A NEW CANADIAN ENDOUROLOGY GROUP STENT SYMPTOM SCORE (CEGSSS) Naeem Bhojani, Jason Y. Lee, Shubhadip K. De, Andrea G. Lantz, Sri Sivalingam, Michael Ordon, Kymora B. Scotland, Sero Andonian, and Ben H. Chew Naeem BhojaniNaeem Bhojani More articles by this author , Jason Y. LeeJason Y. Lee More articles by this author , Shubhadip K. DeShubhadip K. De More articles by this author , Andrea G. LantzAndrea G. Lantz More articles by this author , Sri SivalingamSri Sivalingam More articles by this author , Michael OrdonMichael Ordon More articles by this author , Kymora B. ScotlandKymora B. Scotland More articles by this author , Sero AndonianSero Andonian More articles by this author , and Ben H. ChewBen H. Chew More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001973.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Placement of a ureteral stent is a common urological intervention that can significantly impact QoL. The ureteral stent symptom questionnaire (USSQ) has been used since its development in 2003 but its length hinders its use in clinical practice. Therefore, we sought to develop a new, shorter, practical and validated tool. METHODS: We sought to create a new quality of life questionnaire using a 2 phase schema: Phase 1: Using the USSQ as a starting point, 9 Canadian Endourology Group (CEG) members and 21 patients evaluated each item of the USSQ (Likert scale 1 to 5) to determine its importance/relevance in assessing symptoms associated with a ureteral stent. All accepted items were than discussed with face-to-face meetings. Phase 2 (Pilot trial): Patients undergoing stent placement completed the newly developed CEGSSS in addition to a short survey evaluating the tool itself. 5 rounds of 5 patients, with modifications of the CEGSSS based on feedback after every round was anticipated. RESULTS: Phase 1: After consultation with patients and CEG experts, items were accepted if the mean patient or expert rating was ≥4.0 (out of 5) with a SD of ≤0.75. Questions with mean patient or mean expert ratings of ≥4.0 but SD ≥0.75 or with divergent results between patients and experts were flagged for discussion. Those items that did not meet these requirements were rejected. The final CEGSSS contains 15 questions divided into 3 domains: Urination (8 Q), Pain (3 Q) and QoL (2 Q). Phase 2: 16 patients were recruited. After 3 rounds of patient feedback, no new feedback was received and therefore this process was deemed complete (Table 1). Median time to complete the CEGSSS was 7 minutes (2-20 minutes). 2/16 patients required assistance to complete the questionnaire, but all patients rated it as easy to navigate. Mean level of difficulty was 1.75/5. CONCLUSIONS: Through a process of expert and patient consultation and a pilot trial, a new stent symptom questionnaire was developed that is short, easy for patients to understand and clinically relevant. The next step will be external validation of the newly developed CEGSSS. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e93-e94 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Naeem Bhojani More articles by this author Jason Y. Lee More articles by this author Shubhadip K. De More articles by this author Andrea G. Lantz More articles by this author Sri Sivalingam More articles by this author Michael Ordon More articles by this author Kymora B. Scotland More articles by this author Sero Andonian More articles by this author Ben H. Chew More articles by this author Expand All Advertisement Loading ...

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0470.018

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 head0.263
Teacher spread0.233 · 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
GenreMethods

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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