Efficacy and safety of hydroxychloroquine for the treatment of osteoarthritis: protocol for a systematic review of randomized controlled trials
Bibliographic record
Abstract
Abstract Hydroxychloroquine (HCQ) is a conventional disease-modifying antirheumatic drug (DMARD), which is considered as relatively safe, and offers a modest efficacy profile for the treatment of inflammatory rheumatic diseases such as rheumatoid arthritis and systemic lupus erythematosus. In view of the anecdotal evidence on its immunomodulatory and anti-inflammatory properties, HCQ has been used as an off-label option in patients with osteoarthritis (OA), mainly for the treatment of inflammatory OA. Recently, many investigators have evaluated the safety and efficacy of HCQ for the treatment of OA in various randomized control trials (RCTs). While most RCTs have evaluated the HCQ in inflammatory OA (erosive hand OA), there are studies constituting knee OA patients as well. Currently, there are no systematic reviews that have summarized the evidence on the efficacy and safety of HCQ in OA population. Hence, this study aims to systematically review the evidence from RCTs assessing the efficacy and safety of HCQ for the treatment of OA. Biomedical databases such as PubMed, Embase, and Google Scholar will be searched to identify the RCTs of HCQ in patients with OA (hand, knee, hip, or any other OA). Cochrane risk of bias tool will be used to assess the quality of included studies. Review Manager 5 (Rev Man) and STATA Version 16 will be used to conduct the statistical analysis.
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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 | 0.079 | 0.080 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.008 |
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".