Optimal Treatment Interval of Viscosupplementation for Osteoarthritic Knee Pain: Real-world Evidence from a Retrospective Study
Bibliographic record
Abstract
Background: The evidence supporting multiple courses of viscosupplementation for knee osteoarthritis continues to grow; however, the optimal treatment interval for repeat courses is not well understood. To address this, we compared baseline pain and disability scores in patients returning for subsequent treatment with their prior discharge scores. Methods: We retrospectively collected data from patients at 16 rehabilitation clinics who presented for repeated courses of viscosupplementation treatment for knee OA. Primary outcomes were pain (visual analog scale, VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores, which were collected following the initial treatment course and compared with scores upon return for treatment. The proportion of patients who fulfilled a minimal clinically important difference in each outcome was calculated. Results: 61 patients (81 knees) were included in our analysis. After a 6-month treatment interval, no significant differences were noted between post-discharge and returning scores for either VAS (p=0.73) or WOMAC (Pain: p=0.42; Function: p=0.54; Stiffness: p=0.29). Patients waiting 9 months to return for treatment saw a 45% increase in their pain scores (p=0.10) and significant worsening in WOMAC scores (Pain: p=0.007; Function: p=0.03; Stiffness: p=0.04). At 12 months, pain (p=0.01), WOMAC Pain (p=0.05), and WOMAC Stiffness (p=0.02) had all worsened significantly compared to discharge following the initial course. Conclusion: Our data indicate that patients who return for treatment within a 6-month treatment interval maintain their improvements, but that when the interval increases to 9 months or more, patients present as significantly worsened, having lost the benefit of their initial course 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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".