One-year follow-up of efficacy and cost of repeated doses versus single larger dose of intra-articular hyaluronic acid for knee osteoarthritis
Bibliographic record
Abstract
Purpose: A recent 3-month randomized, open-label controlled trial found that the intra-articular hyaluronic acid injection (GO-ON ® ) given as a single dose of 5 mL is as effective and safe as three repeated doses of 2.5 mL in patients with knee osteoarthritis. However, the information on the long-term efficacy and economic implications of the single-dose regimen is still limited. Hence, this follow-up study was designed to compare the effectiveness and costs of the two regimens 12 months following the treatment. Methods: All the 127 patients, who received either three repeated doses ( n = 64) or a single dose ( n = 63) of GO-ON in the previous trial, were followed up in month 12 following the treatment. The effectiveness of both the regimens was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the mean WOMAC scores were compared with those recorded at the baseline and in month 3. Additionally, the total treatment costs of the two regimens, taking account of both direct and indirect costs, were computed and compared. Results: A total of 125 patients (98.4%) completed the assessment. Despite the reduction of the overall mean WOMAC score from 39.24 to 19.93 ( p < 0.001) in the first 3 months following the treatment with GO-ON, no further changes were observed up to month 12 ( p > 0.95). In the meantime, the two regimens did not differ in the mean WOMAC scores ( p = 0.749) and in the subscale scores for pain ( p = 0.970), stiffness ( p = 0.526), and physical functioning ( p = 0.667) in month 12. The cost for single-dose injection was found to be approximately 30% lower compared to the repeated doses. Conclusion: These findings indicate that the single larger dose of GO-ON is as effective as the repeated doses over 12 months, and yet the total treatment cost is lowered.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".