The X-Y factor: Females and males with urologic chronic pelvic pain syndrome present distinct clinical phenotypes
Bibliographic record
Abstract
INTRODUCTION: Urological chronic pelvic pain syndrome (UCPPS) in females is often attributed to the bladder (interstitial cystitis/ bladder pain syndrome), while UCPPS in males is often attributed to the prostate (chronic prostatitis/chronic pelvic pain syndrome). However, there is increasing awareness that bladder pain plays a role in both males and females and the degree of overlap of clinical characteristics in males and females with UCPPS is not well known. Our objective was to compare clinical phenotypes of females and males with UCPPS. METHODS: We conducted a retrospective analysis of prospectively collected data from a single-centre patient population presenting between 1998 and 2016 to our UCPPS clinic. Demographics, symptom scores, pain scales, retrospectively described clinical UPOINT (urinary, psychosocial, organ-specific, infection, neurogenic, and tenderness) scoring, and presence of comorbid medical conditions were compared between females and males using comparative analyses. RESULTS: We identified 2007 subjects (1523 males, 484 females) with UCPPS. Females had increased prevalence of irritable bowel syndrome (25% vs. 11.2%), chronic fatigue syndrome (13.6% vs. 1.6%), fibromyalgia (16.9% vs. 1.6%), drug allergies (56.6% vs. 13.5%), diabetes (20.2% vs. 3.9%), depression (31% vs. 18.4%), and alcohol use (44.2% vs. 10.8%) compared to males with UCPPS (all p<0.001). In respect to UPOINT domains, females had a higher "total" (3.2 vs. 2.4), "urinary" (92.8% vs. 67.6%), "organ-specific" (90.1% vs. 51.4%), and "neurogenic" (44.7% vs. 30%) prevalence compared to males (all p<0.001). CONCLUSIONS: Females with UCPPS have greater prevalence of systemic disorders/symptoms and worse urinary symptoms than males with UCPPS. These findings demonstrate that females and males with UCPPS have distinct and different clinical phenotypes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".