One shot of hyaluronic acid in advanced knee osteoarthritis: postmarketing clinical follow-up for real-world evidence in a pain unit
Bibliographic record
Abstract
BACKGROUND: this study aims to assess the real-world efficacy and safety of one shot of intraarticular (IA) hyaluronic acid (HA) in patients with advanced knee osteoarthritis (OA), in accordance with the requirement of the new EU Regulation on medical devices to monitor the safety and efficacy of throughout their life cycle. METHODS: observational, cross-sectional and retrospective study in a cohort of patients with advanced knee OA treated in a Pain Unit with a single injection of IA HA between January 2021 and December 2022. Efficacy was assessed at 6 months using the Visual Analogue Scale (VAS) for pain, and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for pain, stiffness and function. The Research Ethics Committee of the Autonomous Community of Aragon (CEICA) approved the study. Patient's data were pseudonymized. RESULTS: the study included a cohort of 30 patients (mean age 73.1, 63.3% female, 66.7% retired). At 6 months, patients showed 51.3% and 52.2% reduction in pain (VAS and WOMAC, respectively), 44.9% improvement in function and 60% improvement in stiffness. In addition, 83.3% of patients showed improvement ≥ 20% according to VAS and 80% of patients according to total WOMAC. No adverse events were informed. CONCLUSION: the results support the use of IA HA as an effective and safe treatment for advanced knee OA, providing significant improvements in pain, stiffness, and function over six months. Future research should include larger populations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".