Comparison of Intra-articular Injection of Hyaluronic Acid and <i>N</i> -Acetyl Cysteine in the Treatment of Knee Osteoarthritis: A Pilot Study
Bibliographic record
Abstract
Objective To compare the relative effectiveness of intra-articular N-acetyl cysteine (NAC) and hyaluronic acid (HA) on pain, function and cartilage degradation markers in patients with mild to moderate knee osteoarthritis (OA). Design We prospectively conducted a clinical trial with 20 patients having a diagnosis of Kellgren-Lawrence grade 2-3 knee OA, and randomly allocated to the HA or NAC groups. Groups were matched on age, sex, and body mass index. Injections of 3-mL HA (Hylan G-F 20) or 3-mL NAC (Asist ampoule) were administered as a single shot. Functional status and pain were evaluated before and after injection, using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the visual analogue scale (VAS) scores. Pre- and posttreatment concentrations of serum C-reactive protein (CRP), synovial fluid chondroitin-6-sulfate (C-6S), matrix metalloproteinase-3 (MMP-3), cross-linked C-terminal telopeptide of type 2 collagen (CTX-II), total oxidant status (TOS), and total antioxidant concentration (TAC) were obtained. Results WOMAC, VAS scores, and CRP levels were comparable between groups prior to treatment. Both HA and NAC produced comparable reductions in TOS and MMP-3. NAC was more effective in reducing C-6S and CTX-II ( P < 0.05). No effects on TAC were noted. Conclusions NAC is effective in lowering some cartilage degradation markers, with comparable outcomes to HA for pain and function. NAC could provide a cheaper alternative to HA for intra-articular injection treatment of mild to moderate knee OA. Future placebo controlled trials are warranted to evaluate effectiveness in a larger patient population with a wider range of age and OA severity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".