Investigating the Efficacy and Safety of Diacerein in the Management of Knee Osteoarthritis with reference to its conventional management.
Bibliographic record
Abstract
BACKGROUND: Designated as a "priority disease" by World Health Organization, Osteoarthritis is the most common chronic rheumatic disease. Providing a proper treatment for Osteoarthritis is still a major public health challenge. Diacerein has been proposed as a slow acting, symptom modifying or even disease modifying drug used in Osteoarthritis having a risk-benefit ratio far better than conventionally used drugs. However, the evidence of efficacy and safety of use of Diacerein in Osteoarthritis is yet to be explored. Hence, this study attempted to investigate the efficacy and safety of Diacerein in the management of knee osteoarthritis. METHODS: This is an analytical cohort study comparing Diacerein with Non-steroidal anti-inflammatory drugs for two months in the management of knee OA. Efficacy was assessed by scores of Lysholm Knee Scoring Scale, Knee injury and Osteoarthritis Outcome Score - Physical Function Short form and Western Ontario and McMaster Universities Osteoarthritis Index. RESULTS: After two months of treatment, the post- treatment scores were significantly superior to the baseline scores in both the treatment groups (p<0.001). There were no significant differences among the post-treatment scores in two different treatment groups (p>0.05). Discoloration of urine and gastritis were the frequently reported adverse effects in Diacerein treatment group and Non-steroidal anti-inflammatory drugs treatment group respectively. CONCLUSIONS: Our findings have shown Diacerein is as effective as Non-steroidal anti-inflammatory drugs in treating knee OA patients. Diacerein was generally well tolerated, with a good safety profile. These findings indicate the need for further studies with experimental study design in larger scale.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".