Efficacy and safety of total glucosides of paeony for rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by erosion of joints and surrounding tissues. RA not only causes the decline of patients' physical function and quality of life, but also brings huge economic burden to patients' families and society. Total glucosides of paeony (TGP) is commonly used in treating RA in China. At present, there are many clinical reports about this medicine, but these reports have their own flaws. Therefore, there is an urgent need for systematic review and meta-analysis of the existing clinical evidence. METHODS AND ANALYSIS: Literature search will be carried out in 6 databases, and the literatures will be screened according to the inclusion and exclusion criteria. The clinical effective rate will be taken as primary outcome. Serum rheumatoid factor, C-reactive protein, erythrocyte sedimentation rate, Western Ontario and McMaster before and after treatment and adverse effects will be secondary outcomes. The heterogeneity of the study will be examined by χ and I test. To identify the source of heterogeneity, subgroup analysis will be carried out. The sensitivity test will be conducted investigate the stability of results. Funnel plot and Egger test will be used to evaluate publication bias. Finally, the quality of evidence will be summarized. RESULTS: The results will be published in peer-reviewed journals. CONCLUSIONS: This study will systematically evaluate the efficacy of TGP in the treatment of RA. The results of this study can better guide clinical practice. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/85QVF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".