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Record W4411665610 · doi:10.1002/nau.70098

Interstitial Cystitis/Bladder Pain Syndrome Patient Phenotyping

2025· review· en· W4411665610 on OpenAlexaff
Dylan T. Wolff, Stephen Tranchina, Andrew Schrepf, R. Christopher Doiron, Stephen J. Walker

Bibliographic record

VenueNeurourology and Urodynamics · 2025
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsInterstitial cystitisBladder Pain SyndromeMedicineNarrative reviewPelvic painDiseaseClinical trialIntensive care medicinePhysical therapyBioinformaticsInternal medicinePathologyAlternative medicineSurgery

Abstract

fetched live from OpenAlex

AIMS: In April of 2025, the Wake Forest Institute for Regenerative Medicine hosted a Global Consensus meeting on IC/BPS in Winston-Salem, NC. The goal of this meeting was to establish global consensus regarding diagnostic criteria, phenotyping, treatment outcome assessment, and etiopathology in interstitial cystitis/bladder pain syndrome (IC/BPS). Our sub-committee was tasked with developing a consensus document on patient phenotyping in IC/BPS. METHODS: Narrative review. RESULTS: Patients with IC/BPS populate broad groups that have been characterized as having a bladder-focused disease phenotype (bladder-centric), a widespread pain and symptoms phenotype (systemic), or by other variable phenotypes including those with myofascial pelvic pain. In this review, we discuss the published evidence supporting each of these patient phenotypic groups. CONCLUSIONS: Future clinical trials and treatment development in IC/BPS should include patient phenotyping efforts with, at minimum, a focus on stratification into bladder-centric vs systemic and efforts to refine discriminative thresholds (cut-off points) that may influence differential treatment outcomes. It is important to continue to investigate the importance of patient phenotypes on treatment strategy selection, outcomes, and our understanding of the underlying pathophysiology for this disease spectrum. CLINICAL TRIAL REGISTRATION: N/A.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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.

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

Citations5
Published2025
Admission routes1
Has abstractyes

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