Management of chronic prostatitis/chronic pelvic pain syndrome
Bibliographic record
Abstract
�1.7 (95% confidence interval [CI], �2.8 to �0.6), �1.1 (95% CI, �1.8 to �0.3), �1.4 (95% CI, �2.3 to �0.5), and �1.0 (95% CI, �1.8 to �0.2), respectively. Patients receiving -blockers or anti-inflammatory medications had a higher chance of favorable response compared with placebo, with pooled RRs of 1.6 (95% CI, 1.1-2.3) and 1.8 (95% CI, 1.2-2.6), respectively. Contour-enhanced funnel plots suggested the presence of publication bias for smaller studies of -blocker therapies. The network meta-analysis suggested benefits of antibiotics in decreasing total symptom scores (�9.8; 95% CI, �15.1 to �4.6), pain scores (�4.4; 95% CI, �7.0 to �1.9), voiding scores (�2.8; 95% CI, �4.1 to �1.6), and quality-of-life scores (�1.9; 95% CI, �3.6 to �0.2) compared with placebo. Combining-blockers and antibiotics yielded the greatest benefits compared with placebo, with corresponding decreases of �13.8 (95% CI, �17.5 to �10.2) for total symptom scores, �5.7 (95% CI, �7.8 to �3.6) for pain scores, �3.7 (95% CI, �5.2 to �2.1) for voiding, and �2.8 (95% CI, �4.7 to �0.9) for quality-of-life scores. Conclusions -Blockers,antibiotics,andcombinationsofthesetherapiesappeartoachieve the greatest improvement in clinical symptom scores compared with placebo. Antiinflammatory therapies have a lesser but measurable benefit on selected outcomes. However, beneficial effects of -blockers may be overestimated because of publication bias.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".