Efficacy and safety of diacerein and celecoxib combination therapy for knee osteoarthritis: A double-blind, randomized, placebo-controlled prospective study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is a degenerative disease with a growing burden in South Korea. Corresponding drugs are commonly used for pain relief and joint function improvement. Specifically, symptomatic slow-acting drugs for osteoarthritis are frequently used, with diacerein being the most common symptomatic slow-acting drugs for osteoarthritis in South Korea. In this study, we evaluated the efficacy and safety of diacerein and celecoxib combination therapy in patients with osteoarthritis. METHODS: A total of 71 subjects were randomly assigned to group 1 (diacerein and celecoxib), 2 (diacerein and placebo), or 3 (celecoxib and placebo). The primary outcome measure was the change in the visual analog scale (VAS) score 12 weeks after treatment. RESULTS: The combination therapy group exhibited a significant decrease in the VAS score, alongside the other control monotherapy groups. Although there was no significant difference between the groups, the combination therapy group exhibited a greater decrease in the absolute value of the VAS score than the other groups. Four weeks after treatment, the combination therapy group showed significantly higher improvement in the stiffness and physical function categories of the Western Ontario and McMaster Universities Osteoarthritis Index than the other groups. Additionally, no serious adverse events occurred following combination therapy, with most adverse events being mild and resolving without specific treatment. CONCLUSIONS: Diacerein and celecoxib combination therapy is as safe and effective as corresponding monotherapies. A relatively early improvement in stiffness and physical function following treatment with this combination therapy indicates that physicians should consider this for the early-stage treatment of patients with symptomatic osteoarthritis.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".