Efficacy and Safety of Celecoxib and a Korean SYSADOA (JOINS) for the Treatment of Knee Osteoarthritis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The efficacy of cyclooxygenase-2 (COX-2) inhibitors, including celecoxib, in managing knee osteoarthritis (KO) is well-established. Recently, the plant extract cocktail JOINS (SKI306X and its newer formulation, SKCPT) has been shown to be an effective slow-acting drug for KO. Aims: To compare the efficacy and safety of celecoxib and JOINS in patients with KO. Methods: A systematic search of the MEDLINE, Embase, and Cochrane Library databases identified randomized controlled trials (RCTs) assessing the effectiveness and safety of celecoxib and JOINS. The outcomes included pain relief, functional improvement, and safety profiles. Outcome measurements were compared between the celecoxib and JOINS cohorts at the short-term (closest to 3 months) and mid-term (closest to 12 months). Results: Overall, 23 RCTs involving 3367 patients were included in this systematic review. The efficacy of JOINS in reducing pain, as indicated by the visual analog scale (VAS) score, was comparable to that of celecoxib. Regarding functional improvement assessed using the Western Ontario and McMaster University Arthritis Index (WOMAC), JOINS showed improvement comparable to that of celecoxib regardless of follow-up. In addition, no significant difference was observed in the incidence of adverse events between the celecoxib and JOINS cohorts. Conclusions: The results of this study suggest that JOINS could be considered as a pharmacological agent with significant efficacy for pain relief and functional improvement in patients with KO in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".