Efficacy and safety of drug combinations for chronic pelvic pain: a systematic review
Bibliographic record
Abstract
Clinical disorders associated with chronic pelvic pain (CPP) cause demonstrable emotional and physical dysfunction as well as increased health care utilization. Interventions that have been studied for the treatment of CPP often provide inadequate relief and/or intolerable adverse effects. The common practice of combining multiple CPP treatments needs more supportive evidence, and emerging combination trials have been evaluated in this systematic review. We searched MEDLINE and EMBASE and CENTRAL databases for CPP combination trials. This review included double-blind randomized controlled trials comparing combinations of 2 or more agents to at least 1 monotherapy in adults with CPP. The primary outcome was reduction in pain intensity or pain relief, and secondary outcomes included adverse events, quality of life, and other symptoms. Risk of bias was assessed. Nine studies (1,299 participants) were included and involved various different treatments including ciprofloxacin, tamsulosin, pentosan polysulfate, hyaluronic acid, chondroitin, hydroxyzine, troxerutin, carbazochrome, linzagolix, and allopurinol. Studies were heterogenous according to several features including studied treatments, dose and route of administration, and underlying condition such that no studies could be combined for meta-analysis. None of the included studies reported a significant difference in reducing pain intensity for combination therapy vs monotherapy. If future proof-of-concept studies demonstrate that a given combination is superior to all monotherapy components, subsequent large, double-blind randomized, controlled clinical trials of such combinations for CPP are required to better elucidate the role of combination therapy in clinical settings.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".