Interstitial Cystitis/Bladder Pain Syndrome Patient Phenotyping
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".