Assessment of the Response Profile to Hyaluronic Acid Plus Sorbitol Injection in Patients with Knee Osteoarthritis: Post-Hoc Analysis of a 6-Month Randomized Controlled Trial
Bibliographic record
Abstract
In a previous randomized trial, the non-inferiority of two hyaluronic acid injections (Synolis VA versus Synvisc-One) was assessed in patients with knee OA, with a response rate of 79% for Synolis VA. To assess whether a responder profile could be established for this treatment modality, we used the Synolis VA arm of a published 6-month prospective, multicenter, comparative, randomized, double-blinded trial. At baseline and during the study, pain and function were assessed using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaire. Ninety-six subjects from the intention-to-treat trial were included in the analysis. The 6-month change of WOMAC Pain with Synolis VA was not associated with any baseline clinical data. However, the change in WOMAC Function was significantly associated with its baseline level, even after adjustment for potential confounding variables (p = 0.028), i.e., a poorer physical function at baseline was associated with a better response. In conclusion, in addition to the high absolute response rate to Synolis VA, the probability of success is even increased if administered in patients with more limited physical function at baseline. Further research with other potential confounding clinical variables is warranted in order to better applicate the concept of personalized medicine.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".