Role of diacerein in osteoarthritis.
Bibliographic record
Abstract
Objectives: To evaluate the role of Diacerein among patients with OA. Study Design: Retrospective Case Series. Setting: Mayo Hospital, Lahore. Period: April 2020 to September 2020. Material & Methods: The study was conducted among forty patients with OA (grade II to IV according to ACR criteria) at Department of Rheumatology (EMW), Mayo Hospital, Lahore. Baseline WOMAC (Western Ontario and McMaster Universities Arthritis Index) and VAS (Visual Analogue Scale) was noted. Diacerine, 100mg in bd (twice a day) dose was given for 6 months. After 6 months, WOMAC and VAS were noted and %age improvement was calculated. Results: The mean WOMAC at presentation was 48.78+6.42 and after treatment was 36.20+20 (p<0.05). The mean VAS before and after treatment was 5.88+1.20 and 3.58+3.22, respectively (p<0.05). A 20% improvement was seen among 40% patients. The efficacy of the drug was labeled as yes in 40% patients. One (2.5%) patients suffered from diarrhea, and one (2.5%) patient had raised LFTs after treatment. Conclusion: Diacerine significantly improves the mean WOMAC and VAS score after 6 months of therapy. The efficacy is also high. So, it can be considered as an alternative drug among symptomatic patients with OA in whom the symptoms do not improve after conventional analgesics.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".