Efficacy of Intra-Articular Injection of Biofermentation-Derived High-Molecular Hyaluronic Acid in Knee Osteoarthritis: An Ultrasonographic Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to evaluate the efficacy of intra-articular injection with HYAJOINT Plus, a biofermentation-derived, high-molecular hyaluronic acid (HA), on the progression of structural changes of cartilage in patients with knee osteoarthritis (OA) by using objectively promised ultrasonography (US) evaluation. DESIGN: In this prospective clinical trial, 56 OA patients completed the study. One single dose of injection of HYAJOINT Plus into the knee cavity was performed. The primary efficacy outcome measure for structural change of knee joint was evaluated by US using a semiquantitative grading system. Secondary efficacy outcome measures included Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total and subscale scores. All efficacy outcomes were measured at baseline and at first, third, and sixth month following treatment. RESULTS: There were significant US grade-improvement changes of cartilage between baseline and follow-up visits over medial femoral condyle and transverse overall evaluation at 3- and 6-month follow-ups, and over lateral femoral condyle, intercondylar notch, and medial longitudinal area at 6-month follow-up. The improved score change of WOMAC from baseline was significant at 1- and 3-month follow-ups in pain subscale, whereas score change from baseline was significant at 6-month follow-up in total score and all 3 subscale scores. CONCLUSIONS: It was determined that significant improvement was found on cartilage by US after intra-articular injection with high-molecular weight, biological fermentation-derived HYAJOINT Plus. The semiquantitative grading system by US is a promising tool to identify the efficacy on cartilage band after interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".