Risk factors for severe knee osteoarthritis and efficacy of diacerein in obese vietnamese patients: A randomized, single-blind, noncontrolled clinical trial
Bibliographic record
Abstract
BACKGROUND: There have not been many studies evaluating the results of Diacerein treatment in patients with knee osteoarthritis (OA) and obesity, particularly comparing dosage levels of 50 and 100 mg/day. METHODS: A randomized, single-blind, noncontrolled clinical trial was conducted at the Ho Chi Minh City Hospital for Rehabilitation, Vietnam, from January 2023 to January 2024. Patients with odd-numbered records received Diacerein 50 mg/day, while those with even-numbered records received 100 mg/day. Primary outcomes included symptom and functional improvements, assessed using visual analogue scale (VAS) and Western Ontario and McMaster University Osteoarthritis (WOMAC) scores at weeks 4 and 8. RESULTS: A total of 256 patients (average age 58.3 ± 12.0 years; 59.4% female) were included, and 53.1% reported moderate pain, with average VAS and WOMAC scores of 64.7 ± 14.4 mm and 51.7 ± 11.4 points, respectively. Increased waist circumference, triglycerides (TG), and TG/high density of lipoprotein cholesterol ratio were identified as risk factors for severe knee OA, with respective odds ratio of 4.9 (1.6-14.7), 47 (12.5-176.1), and 22.5 (6.7-75.9). Diacerein significantly improved symptoms and joint function, with the 100-mg/day group showing faster and greater average reductions in VAS and WOMAC scores at weeks 4 and 8. The most notable reduction was 26.7 ± 11.3 for VAS and 20.7 ± 8.9 for WOMAC in week 4. CONCLUSION: Increased waist circumference, plasma TG, and TG/high density of lipoprotein cholesterol ratio raise the risk of severe knee OA. Diacerein treatment improves symptoms and joint function in patients with knee OA accompanied by obesity, with the 100-mg/day dose showing earlier and more effective improvement than the 50-mg/day dose.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".