Diacerein versus non-steroidal anti-inflammatory drugs in the treatment of knee osteoarthritis: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a common musculoskeletal condition affecting millions of people worldwide and posing a significant challenge to clinicians and researchers. Emerging evidence suggests that the multifaceted symptomatology of KOA may be alleviated by diacerein. With this in mind, we conducted a systematic review and meta-analysis to evaluate the efficacy and safety of diacerein in patients with KOA. METHODS: We systematically searched Embase, PubMed, Cochrane Library, Web of Science, Chinese Biomedical Literature Database (CBM), Wanfang Database (WanFang), China National Knowledge Infrastructure (CNKI), and China Science and Technology Journal Database (VIP) from their inception to August 2022 for randomized controlled trials (RCTs) of diacerein intervention on patients with KOA. Two reviewers independently performed the selection of eligible studies and the extraction of relevant data. The meta-analysis was performed using RevMan 5.4 and R 4.1.3 software tools. Depending on the type of outcome indicator selected, summary measures were expressed as mean differences (MD), standardized mean differences (SMD), or odds ratio (OR) with 95% confidence intervals (CI). RESULTS: Twelve RCTs with 1732 patients were included. The results revealed that diacerein had comparable efficacy to non-steroidal anti-inflammatory drugs (NSAIDs) in reducing pain indicators such as Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (SMD = 0.09, 95% CI [-0.10, 0.28], P = 0.34) and visual analogue scale (VAS) (SMD = -0.19, 95% CI [-0.65, 0.27], P = 0.42). However, diacerein outperformed NSAIDs in terms of global efficacy assessment by both patients and investigators (patients: 1.97, 95% CI [1.18, 3.29], P = 0.01; investigator: 2.18, 95% CI [0.99, 4.81], P = 0.05) at the end of treatment and sustained effectiveness in reducing WOMAC score and VAS score at four weeks after treatment. Moreover, there was no significant difference in adverse events incidence between the diacerein and NSAID groups. However, the GRADE evaluation indicated that the majority of the evidence quality was low. CONCLUSIONS: The results of this study suggest that diacerein could potentially be considered as a pharmacological agent with significant efficacy for the treatment of patients suffering from KOA, offering a potential alternative treatment strategy for those patients contraindicated to NSAIDs. However, further high-quality studies with longer follow-up are needed to make more informed decisions about its efficacy in the treatment of KOA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".