Classification systems for chronic pelvic pain in males: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the classification systems for male chronic pelvic pain (CPP). METHODS: The Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Web of Science were searched. Any publication, with no restriction to publication date, was eligible. Publications had to propose a classification system for CPP in males or provide additional information of a system that had been identified. Systems were assessed with an adapted Critical Appraisal of Classification Systems tool. RESULTS: A total of 33 relevant publications were identified, with 22 proposing an original classification system. Systems aimed to: (i) diagnose CPP and/or differentially diagnose CPP from other conditions, (ii) differentially diagnose subtypes within CPP, or (iii) identify features that could inform underlying mechanisms and/or treatment selection. Conditions referred to as chronic prostatitis/chronic pelvic pain syndrome and interstitial cystitis/bladder pain syndrome were most represented. Clinical signs/symptoms, pathoanatomical investigations, and presumed pain mechanisms were used for classification. Quality of systems was low to moderate, implying limitations to consider for their interpretation. CONCLUSIONS: Many classification systems for CPP in males exist. Careful consideration of their intended purpose is required. Future work should examine whether outcomes for patients are improved when decisions are guided by their use.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".