ROLE OF DIACERIN IN DIFFERENT GRADES OF OSTEOARTHRITIS
Bibliographic record
Abstract
Osteoarthritis (OA) is a joint disease that causes pain and degeneration in the affected area. There are no standard guidelines for agents that can slow down or modify the disease's progress. Diacerin is one such agent used to improve symptoms of OA. The objective was to analyze its efficacy in different grades of OA. A quasi-experimental study was conducted at Ghurki Trust Teaching Hospital in Lahore over six months from Jan 2023 to July 2023. After obtaining informed consent and meeting inclusion and exclusion criteria, we included 78 patients with grade II and III OA (39 each). All patients were given Diacerin (100mg twice daily). The baseline Western Ontario and McMaster Universities Arthritis Index (WOMAC) and pain on the Visual Analogue Scale (VAS) were used to measure the treatment's efficacy in improving symptoms after six months. The data was analyzed using SPSS 23.0. The mean age of the participants was 53.98+6.17 years. The mean WOMAC score was 49.62+7.81 before treatment, which decreased significantly to 38.34+8.79 after six months of treatment (p-value <0.05). The mean VAS score before and after treatment was 7.41+0.98 and 4.82+1.07, respectively. These findings suggest that Diacerein significantly reduces pain and improves functional ability in patients with OA of the knee joint. This treatment was safe and well-tolerated. Its use is recommended in early grades of OA.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".