Diacerein as adjuvant to diclofenac sodium in osteoarthritis knee
Bibliographic record
Abstract
AIM: To evaluate clinical effectiveness of diacerein as an adjuvant to diclofenac sodium in treatment of Indian patients with symptomatic osteoarthritis (OA) knee. METHODS: This is a prospective, double-blind, placebo-controlled and intention-to-treat study. An initial washout period of 1 week, was followed by 3 months treatment period during which patients were randomly divided to receive either capsule diacerein 50 mg or matched placebo once daily for the first month and twice daily for the next 2 months with diclofenac sodium 75 mg sustained release tablet once daily given to both groups. Patients were observed for one more month, using paracetamol as rescue therapy. Treatment efficacy was assessed by a visual analogue scale (VAS) and the Western Ontario and McMaster University (WOMAC) Osteoarthritis Index, patient and physician global assessment of OA, daily paracetamol intake. RESULTS: Of 84 patients screened, 74 patients formed the intent-to-treat population (37 patients in each group). At baseline, both groups were comparable and at the third month functional index and pain intensity were better in the diacerein group (VAS 15.33 ± 5.07; WOMAC 15.9 ± 2.40) as compared to the placebo group (VAS 22.83 ± 6.90;WOMAC 36.8 ± 2.92; P < 0.05). When analyzed at the fourth month, improvement persisted in the iacerein group (VAS 14.83 ± 5.16; WOMAC 16 ± 2.5) as compared to placebo group (VAS 33 ± 7.72; WOMAC 48.26 ± 3.5; P < 0.05), demonstrating the carry-over effect of diacerein, which was confirmed by lesser paracetamol consumption in the diacerein group (5.967 ± 0.8087) as compared to the placebo group (12.433 ± 2.128; P < 0.05). CONCLUSION: Use of diacerein and diclofenac sodium together decreases pain and improves joint function significantly more than diclofenac alone in OA knee.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".