In Vivo Effect of Celecoxib and Tenoxicam on Oxidant/ Anti-oxidant Status of Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Abstract. The aim of this study was to compare the in vivo effects on free radical metabolism of 2 non-steroidal anti-inflammatory drugs (NSAIDs): tenoxicam, an oxicam preferentially cyclooxygenase-1 (COX-1) inhibitor, and celecoxib, a sulfonamide selective COX-2 inhibitor. The serum levels of oxidative stress-related enzymes (ie, xanthine oxidase (XO), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px)), of a lipid peroxidation marker (malondialdehyde (MDA)), and of nitric oxide (NO) in patients with knee osteoarthritis were studied at baseline and after a 4-wk course of treatment with celecoxib (n = 11) and tenoxicam (n = 12). Celecoxib-treated patients had significant decrease in nitrite levels (p = 0.043), whereas SOD, XO, GSH-Px enzyme activities, and MDA levels did not change significantly compared to baseline. Tenoxicam-treated patients had significant decrease in nitrite levels (p = 0.036) and XO activity (p = 0.01), but their SOD, GSH-Px enzyme activities, and MDA levels were unchanged from baseline. There was significant correlation between the patients ’ (n = 23) Western Ontario and McMaster Universities (WOMAC) LK3.0 Osteoarthritis Index, WOMAC-pain scores, and MDA levels (r = 0.50, p = 0.014) and the patients’ WOMAC-stiffness scores and XO enzyme activity (r = 0.46, p = 0.027) at baseline. Significant improvement was found in pain-VAS, patients ’ global assessment, and WOMAC pain, stiffness, and physical function scores in celecoxib and tenoxicam-treated groups. In summary, our study revealed that tenoxicam may have
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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 | 0.000 |
| 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".