A Subtype Based Analysis of Urological Chronic Pelvic Pain Syndrome in Men
Bibliographic record
Abstract
PURPOSE: The current conceptualization of urological chronic pelvic pain syndrome in men recognizes a wide variety of pain, psychosocial, sexual and urological symptoms and markers that may contribute to decreased quality of life. Unfortunately, this syndrome is difficult to clearly define and treat due to heterogeneous symptom profiles. We systematically describe these heterogeneous symptoms and investigated whether they could be subtyped into distinct syndromes. MATERIALS AND METHODS: A total of 171 men diagnosed with urological chronic pelvic pain syndrome completed validated questionnaires, a structured genital pain interview, digital pain threshold testing and urological assessment. Pain interview results are systematically presented as descriptive information. We used k-means cluster analysis to define subtypes. RESULTS: Seven homogenous, distinct clusters were defined, each with a remarkably different symptom presentation. These clusters were described and related to previous hypotheses of urological chronic pelvic pain syndrome etiology. CONCLUSIONS: These clusters may represent distinct subtypes of urological chronic pelvic pain syndrome that can be used to guide treatment more effectively. Defining subtypes may also improve our understanding of the underlying mechanisms of urological chronic pelvic pain syndrome.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".