Efficacy of generic versus branded diacerein for treatment of knee osteoarthritis: A randomized control trial
Bibliographic record
Abstract
BACKGROUND: Several studies have proved that diacerein effectively treats knee osteoarthritis (OA). All studies used branded diacerein. Recently, generic diacerein has been available in several countries, with limited studies comparing the efficacy of generic and branded diacerein for knee OA treatment. METHODS: Among 200 eligible patients, 94 were randomized to take a daily 50 mg of generic diacerein (Diaceric®); group A or branded diacerein (Artrodar®); group B for treating mild to moderate knee OA. All patients were assigned 5-visit assessments and followed until 24 weeks. The primary outcome was a visual analog scale (VAS) on the motion. The secondary outcomes were 2 patient-report outcome measures (PROMs): the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Short Form-12 (SF-12), as well as 3 performance-based measures (PBMs): 5-time sit to stand test (5 × SST), the time up and go test (TUGT), and the 3-minute walk test (3MWT). RESULTS: There were 47 patients in group A and 47 in group B, with no patients lost for FU. Among all patients, 79.8% were female with a mean age of 63.2 years in group A and 64.8 years in group B. The Kellgren and Lawrence (KL) grade II was the most common in both groups. There were no differences in all demographic data. At 24-week follow-up (FU), both groups had significantly improved VAS, with a 12-week earlier improvement in the branded diacerein. In addition, the PBMs, including 5 × SST and 3MWT, significantly improved from 12-week FU in both groups, with insignificantly improved WOMAC and SF-12 and no serious adverse events in either group. CONCLUSION: After a 24-week FU, the generic diacerein had similar efficacy as the branded diacerein in significantly improving VAS and PBMs: 5 × SST and 3MWT. However, the latter had a faster statistically improved VAS than the former.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".