Single Injection of Cross-Linked Hyaluronate in Knee Osteoarthritis: A 52-Week Double-Blind Randomized Controlled Trial
Bibliographic record
Abstract
Background: to compare the 52-week effectiveness and safety between HYAJOINT Plus (HJP) and Durolane in knee osteoarthritis (OA) treatment. Methods: consecutive patients received a single injection of 3 mL HJP or Durolane. The primary outcome was a visual analog scale (VAS) pain measurement at 26 weeks post-injection. Secondary outcomes included other clinical, satisfaction, and safety assessments for 52 weeks. Results: 142 patients were equally randomized. At week 26, the HJP group had less VAS pain than the Durolane group (18.1 ± 9.5 versus 24.4 ± 14.0, p = 0.001). Both groups showed improvement in their VAS pain and stiffness scores, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and total scores for 52 weeks after injection (p < 0.001). However, the HJP group showed lower VAS pain and stiffness scores, reduced WOMAC pain and stiffness scores, a shorter Timed “Up & Go” (TUG) time, and a higher satisfaction score than the Durolane group for 39 weeks (p < 0.05). Only mild and self-limited adverse events occurred (40.8%). Conclusion: While a single injection of either HJP or Durolane is safe and effective for at least 52 weeks, HJP provided superior improvement in terms of VAS pain and stiffness scores, WOMAC pain and stiffness scores, and satisfaction score within 39 weeks of treatment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".