The Booster Effect of a Single Quarterly Dose of Hyaluronic Acid in Knee Osteoarthritis: Five-Year Results of a Registry-Based Study
Bibliographic record
Abstract
Objective Viscosupplementation by hyaluronic acid (HA) is well established non-surgical treatment of knee osteoarthritis (KOA). This registry-based study investigated the booster effect of a quarterly intra-articular single knee injection (30mg/2ml) for five years. Methods Sixty patients, including 29 males and 31 females, with a mean ± SD age 61.07 ± 9.15 with Kellgren-Lawrence grade I-III KOA, have been selected from a registry of interventional treatments for musculoskeletal pain conditions. To be eligible, patients had to be treated with a single quarterly intraarticular injection of HA with a follow-up of at least five years and assessed with Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Numeric Rating Scale (NRS) at baseline and after each HA injection in the first 24 months and at 36, 48, and 60 months. Results Sixty of 63 patients enrolled in this study completed the 60 months of follow-up. Patients had a marked improvement in knee function and pain, expressed by the significant reduction in WOMAC (T0 48.62±8.95 vs. T11 10.75±4.36; p<0.0001) and NRS scores (6.38±1.06 vs. T11 0.95±0.89 p<0.0001) from the baseline to the end of the follow-up period. Conclusion A quarterly injection of HA provides a rapid, safe, and stable long-term reduction of pain and improvement of function in elderly people with mild to moderate knee osteoarthritis along a five-year period of treatment and follow-up. Further investigations are necessary to confirm these findings.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".