Predictors of Clinical Benefit with Intra-articular Hyaluronic Acid inPatients with Knee Osteoarthritis - A Narrative Review
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence regarding the efficacy of viscosupplementation with intra-articular hyaluronic acid injections in knee osteoarthritis. One possible explanation for the inconsistent findings on its efficacy is that only certain subpopulations of patients benefit from this therapy. OBJECTIVE: The purpose of this narrative review is to succinctly summarize the existing data on the predictive factors of clinical response to intra-articular hyaluronic acid to identify the patient profile most likely to benefit from this therapy. METHODS: For this narrative review, a PubMed search was conducted in January 2023, with no date limits, to identify publications reporting predictive factors of response to viscosupplementation using the following terms: hyaluronic acid OR viscosupplem* AND osteoarthritis AND knee AND predict*. Searches were limited to randomized controlled trials, systematic reviews and meta- analyses, or observational studies written in English. Other relevant references were identified by searching the references of retrieved articles. RESULTS: The disease severity was found to reliably predict response to intra-articular hyaluronic acid injections; patients with less severe disease consistently had a more robust therapeutic response than those with more severe disease. Other clinical variables such as level of baseline pain did not reliably predict response. Body mass index, and possibly age, may also be independent predictors of the response. CONCLUSION: A review of the existing literature suggests that patients with less severe clinical symptoms and radiological findings, who are younger, and with a lower or normal body mass index are the best candidates for intra-articular hyaluronic acid therapy.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".