Knee Viscosupplementation: Cost-Effectiveness Analysis between Stabilized Hyaluronic Acid in a Single Injection versus Five Injections of Standard Hyaluronic Acid
Bibliographic record
Abstract
Given the wide difference in price per vial between various presentations of hyaluronic acid, this study seeks to compare the effectiveness and treatment cost of stabilized hyaluronic acid (NASHA) in a single injection with standard preparations of hyaluronic acid (HA) in five injections in osteoarthritis (OA) of the knee. Fifty-four patients with knee osteoarthritis (Kellgren–Lawrence Grade II and III) and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain score greater than 7, with a homogeneous distribution of age, sex, BMI, and duration of disease, were included in this study. Patients were randomized into two groups: Group I was treated with NASHA (Durolane®) and Group II with HA (Go-ON®). Patient’s evolution was followed up at the 1st, 2nd, 4th, 8th, 12th, and 26th week after treatment. A statistically significant improvement in WOMAC score was observed for patients treated with NASHA versus those who received HA at Week 26. In addition, the need for analgesia was significantly reduced at Week 26 in the NASHA-treated group. Finally, the economic analysis showed an increased cost of overall treatment with HA injections. Our data support the use of the NASHA class of products in the treatment of knee OA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".