Clinical Observation of Diacerein Combined with Celecoxib in the Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
Objective To approach the curative effect of Diacerein combined with celecoxib in the treatment of knee osteoarthritis.Methods From January 2007 to July 2009 210 patients with knee osteoarthritis were selected,which were divided into celecoxib group(group A),Diacerein group(group B) and combination group(group C) randomly.Patients in group A were given celecoxib 200 mg qd for 12 weeks.Patients in group B were given Diacerein 50 mg bid for 12 weeks.Patients in group C were given celecoxib 200 mg bid combined with Diacerein 50 mg bid for 12 weeks.The mean VAS(visual analog scale) of the joint pain on walking for 20 meters and the mean WOMAC Index(the western Ontario mcmaster universities pain index) were used to assess the clinic symptom before and after treatment in the three groups.The patients were observed in 1 week,4 weeks,12 weeks and 24 weeks after treatment.The statistic analysis was done.Results The mean VAS and WOMAC were improved 1 week after treatment in both of group A and C(P0.01) and they were also improved 4 weeks and 12 weeks after treatment in group A,B and C(P0.01).24 weeks after treatment the mean VAS and WOMAC in group C were better than those in group B and they were also better than those prior treatment in both of the two groups(P0.01).There were no harmful incident happened.Conslusion Both Diacerein and celecoxib have the function for the treatment of knee osteoarthritis.Diacerein combined with celecoxib could not only less early pain and other symptom but also make the curative effect more permanent.At the same time they shold be widely used for the treatment of knee osteoarthritis in clinic.
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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 | 0.000 | 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".