Effectiveness of etofenamate for treatment of knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
SavaÅ Güner,1 Mehmet Ata Gökalp,1 Abdurrahim Gözen,1 Seyyid Åerif Ünsal,1 Åükriye Ä°lkay Güner2 1Department of Trauma and Orthopedic Surgery, Medical School, Yuzuncu Yil University, 2School of Health, Yuzuncu Yil University, Van, Turkey Abstract: The intramuscular application of etofenamate in the treatment of knee osteoarthritis was not observed in the existing English language literature. The objectives of this study were to compare the efficacy of etofenamate versus hyaluronic acid (HA) in reducing joint pain and functional improvement for mild to moderate knee osteoarthritis. The patients were randomly divided into etofenamate (n=29) and HA (n=30) groups. Intramuscular etofenamate injection was administered as a series of seven intramuscular injections at intervals of 1 day. Intra-articular HA injection was administered as a series of three intra-articular injections at intervals of 1 week. Clinical evaluation was made before the first injection and again both 6 and 12 months after the last injection. The evaluation consisted of patient-assessed pain on a visual analog scale (VAS) and on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Statistical significance was found for the etofenamate group when comparing preinjection with 12 months postinjection VAS scores (P<0.05). Statistical significance was also found for the HA group when comparing preinjection with 12 months postinjection VAS and WOMAC scores (P<0.05). However, there was no significant difference between the etofenamate and HA groups in terms of VAS or WOMAC scores measured at 12 months after injection (P>0.05). Results from this study indicated that, etofenamate treatment was not significantly more effective than HA treatment. However, both methods were effective and successful in treating knee osteoarthritis. Keywords: knee osteoarthritis, arthralgia, treatment, etofenamate, nonsteroidal anti-inflammatory drugs
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".