Anti-Inflammatory Effects of Intra-Articular Hyaluronic Acid: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is one of the leading causes of disability in the adult population. Common nonoperative treatment options include nonsteroidal anti-inflammatory drugs (NSAIDs), intra-articular corticosteroids, and intra-articular injections of hyaluronic acid (HA). HA is found intrinsically within the knee joint providing viscoelastic properties to the synovial fluid. HA therapy provides anti-inflammatory relief through a number of different pathways, including the suppression of pro-inflammatory cytokines and chemokines. METHODS: We conducted a systematic review to summarize the published literature on the anti-inflammatory properties of hyaluronic acid in osteoarthritis. Included articles were categorized based on the primary anti-inflammatory responses described within them, by the immediate cell surface receptor protein assessed within the article, or based on the primary theme of the article. Key findings aimed to describe the macromolecules and inflammatory-mediated responses associated with the cell transmembrane receptors. RESULTS: Forty-eight articles were included in this systematic review that focused on the general anti-inflammatory effects of HA in knee OA, mediated through receptor-binding relationships with cluster determinant 44 (CD44), toll-like receptor 2 (TLR-2) and 4 (TLR-4), intercellular adhesion molecule-1 (ICAM-1), and layilin (LAYN) cell surface receptors. Higher molecular weight HA (HMWHA) promotes anti-inflammatory responses, whereas short HA oligosaccharides produce inflammatory reactions. CONCLUSIONS: Intra-articular HA is a viable therapeutic option in treating knee OA and suppressing inflammatory responses. HMWHA is effective in suppressing the key macromolecules that elicit the inflammatory response by short HA oligosaccharides.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".