Effect of ultrasound-detected synovitis on therapeutic efficacy of hyaluronic acid injection for symptomatic knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether ultrasound (US)-detected synovitis affects the therapeutic efficacy of hyaluronic acid (HA) injection for treating knee OA. METHODS: Patients with symptomatic knee OA were recruited. All the patients received HA injection two times at 2-week intervals. Clinical assessments were performed using a visual analogue scale (VAS) and the Western Ontario and McMaster Universities OA Index (WOMAC) at baseline and 1 and 6 months after treatment. Imaging evaluation was based on complete knee US examination and the Kellgren-Lawrence grading. Suprapatellar synovial fluid (SF) depth, synovial hypertrophy (SH) and vascularity were measured through US. RESULTS: In total, 137 patients who fulfilled the inclusion criteria were included in the analysis. All patients demonstrated improvement in VAS and WOMAC scores at 1 and 6 months after treatment (P < 0.001). Moreover, regression model-based analysis revealed significant associations of SF depth with the VAS and WOMAC scores in all patients. Each centimetre increase in the effusion diameter was associated with a decrease in the 1-month post-treatment VAS improvement percentage (15.26; 95% CI: 0.05, 29.5; P = 0.042) and 6-month post-treatment WOMAC improvement (37.43; 95% CI: 37.68, 50.69; P < 0.01). However, SH and vascularity were not significantly associated with VAS or WOMAC scores. CONCLUSION: Ultrasound detected suprapatellar effusion predicts reduced efficacy of HA injection in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".