Exploring the Efficacy of Joint Lavage in Knee Osteoarthritis: A Focus on Cytokines, Degrading Enzymes, and Oxidative Stress
Bibliographic record
Abstract
Purpose/Aim This study aimed to assess the effectiveness of joint lavage in managing knee osteoarthritis (OA) by evaluating its effect on pain relief, inflammatory markers, cartilage-degrading enzymes, and oxidative stress. Methods Seventy patients with Kellgren–Lawrence grade 2 or 3 knee OA were selected for this single-center study. Joint lavage was performed, and pain and function were measured using the visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores at baseline and 24 weeks postintervention. Synovial fluid samples were collected at baseline, before lavage, and 24 weeks postintervention. Samples were stored at −80°C and analyzed in batches to minimize variability. At the time of analysis, the samples were thawed and evaluated for levels of proinflammatory cytokines, interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), matrix metalloproteinase-3 (MMP-3), and total oxidant status (TOS), and oxidative stress index (OSI). Results Postintervention, VAS, and WOMAC scores significantly decreased ( P < 0.001), with 100% achieving the minimal clinically important difference (MCID). Patient acceptable symptom state (PASS) rates varied: VAS (80%), WOMAC pain (50%), function (81.4%), and total (84.3%). Cytokine levels (IL-1β, IL-6, TNF-α) and MMP-3 significantly decreased ( P < 0.001), along with TOS and OSI. Baseline TNF-α, IL-6, and IL-1β levels were significantly correlated with improvements in VAS and WOMAC scores. Moderate correlations were observed between reductions in IL-6/TNF-α and improvements in VAS/WOMAC. No significant associations were found between confounders and outcomes. Conclusions Joint lavage resulted in marked pain relief and functional improvement while significantly reducing inflammatory markers, cartilage-degrading enzymes, and oxidative stress.
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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 | 0.001 | 0.001 |
| 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".