Efficacy of Elaeagnus angustifolia extract in the treatment of knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
Osteoarthritis (OA) is one of the most common musculoskeletal disorders all over the world. Available anti-arthritic medications have only partial efficacy and their long-term use is associated with adverse events. Elaeagnus Angustifolia (EA) is a medicinal plant with analgesic and anti-inflammatory properties. The present study evaluated the impact of two doses of EA extract compared with ibuprofen on the severity of disease in patients with knee OA. This study was designed as a randomized, double blind, active-controlled and parallel group trial. Patients with OA were randomized to receive 300 mg/day (n=33) or 600 mg/day (n=32) of EA aqueous extract, or 800 mg/day ibuprofen (n=32) for 7 weeks. EA extract contained 0.21 % (w/w) kaempferol according to HPLC. Efficacy of treatment was assessed using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analogue Scale (VAS) of pain, Lequesne's Pain-Function Index (LPFI), and patient's global assessment (PGA) index. The amount of kaempferol in the extract was determined by HPLC method to be 0.21 % w/w. There were significant reductions in WOMAC, VAS, LPFI and PGA scores by the end of trial with all three interventions. Comparison of the changes in WOMAC, VAS and LPFI scores among the treatment groups did not reveal any significant difference between EA and ibuprofen, and between low and high doses of EA. EA was safe and well tolerated during the course of trial and no adverse event was reported. The present results suggest beneficial effects of aqueous EA extract in reducing the symptoms of OA with an efficacy comparable to that of ibuprofen.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".