Comparative efficacy of non-pharmacological management for chronic prostatitis/chronic pelvic pain syndrome: a systematic review and network meta-analysis protocol
Bibliographic record
Abstract
BACKGROUND: Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) has posed a significant burden on affected individuals and healthcare systems. While pharmacological treatments are commonly used, non-pharmacological management strategies have gained attention for their potential benefits in improving CP/CPPS symptoms. However, the comparative efficacy of these non-pharmacological interventions remains unclear. The aim of this study is to assess the comparative effectiveness of non-pharmacological interventions for CP/CPPS regarding prostatic symptoms. METHOD: This systematic review and network meta-analysis will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search will be conducted in electronic databases, including PubMed, Embase, Cochrane Library and Web of Science, to identify relevant studies. Eligible studies will include randomised controlled trials investigating non-pharmacological management strategies for CP/CPPS. Two independent reviewers will screen the retrieved citations, extract data and assess the risk of bias. Data synthesis will involve performing a network meta-analysis to compare the efficacy of different non-pharmacological interventions while considering both direct and indirect evidence. ETHICS AND DISSEMINATION: The review does not require ethical approval. The findings of the review will be disseminated through publication in an academic journal, presentations at conferences and various media outlets. PROSPERO REGISTRATION NUMBER: CRD42024506143.
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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 | 0.070 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".