#219 : Comparative Efficacy and Safety of Interventions for Pain and Quality of Life in the Treatment of Endometriosis: A Network Meta-Analysis
Bibliographic record
Abstract
Background and Aims: Numerous endometriosis treatments exist, ranging from surgical to traditional medications to complementary and alternative medicines (CAMs). The ranking of treatments across management categories remains unknown. To address this research gap, this network meta-analysis (NMA) aims to rank endometriosis treatments in terms of efficacy and safety for endometriosis-related pain and quality of life. Method: A systematic literature search and fixed effects NMA was conducted. Eight databases were searched from inception to August 2022. All randomised control trials (RCTs) in women with surgically confirmed endometriosis were considered. Results: After screening 8,668 studies, 165 RCTs were included, consisting of 26,762 participants, investigating 48 medical, 13 CAMs and 16 surgical interventions. Preliminary analysis of the CAMs interventions (9 RCTs, n=522) suggested acupuncture (SMD -1.96; 95%CI: -2.63, -1.29), olive oil (SMD -1.68; 95%CI: -2.69, -0.67), fatty acid supplements (SMD -1.48; 95% CI: -2.15, -0.81), contraception + Chinese medicines (SMD -1.28; 95%CI: -2.39, -0.16), Chinese medicine (SMD -0.91, 95%CI: -1.78, -0.04) and vitamin C&E (SMD -0.38; 95%CI -0.89, 0.13) demonstrated a reduction in pain compared to placebo. There was insufficient evidence to detect a difference in pain using Vitamin D compared to placebo (SMD 0.09 95% CI: -0.55, 0.73). Complete results, including the individual and combined categories within a Bayesian framework and intervention ranking, will be available for ASPIRE 2023. Conclusion: This is the first NMA to compare the evidence of all endometriosis treatments. The findings have the potential to guide treatment decisions and guideline development.
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".