Efficacy of Combination of Meloxicam and Pregabalin for Pain in Knee Osteoarthritis
Bibliographic record
Abstract
PURPOSE: Osteoarthritic pain is largely considered to be inflammatory pain. Sensory nerve fibers innervating the knee have been shown to be significantly damaged in rat models of knee osteoarthritis (OA) in which the subchondral bone junction is destroyed, and this induces neuropathic pain (NP). Pregabalin was developed as a pain killer for NP; however, there are no reports on pregabalin use in OA patients. The purpose of this study was to investigate the efficacy of pregabalin for pain in OA patients. MATERIALS AND METHODS: Eighty-nine knee OA patients were evaluated in this randomized prospective study. Patients were divided into meloxicam, pregabalin, and meloxicam+pregabalin groups. Pain scores were evaluated before and 4 weeks after drug application using a visual analogue scale (VAS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Pain scales among groups were compared using a Kruskal-Wallis test. RESULTS: Before drug application, there was no significant difference in VAS and WOMAC scores among the three groups (p>0.05). Significant pain relief was seen in the meloxicam+pregabalin group in VAS at 1, 2, and 4 weeks, and WOMAC score at 4 weeks, compared with the other groups (p<0.05). No significant pain relief was seen in the meloxicam only group in VAS during 4 weeks and WOMAC score at 4 weeks compared with the pregabalin only group (p>0.05). CONCLUSION: Meloxicam+pregabalin was effective for pain in OA patients. This finding suggests that OA pain is a combination of inflammatory and NP.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".