Concurrent Imaging and Clinical Study of the Efficacy of Hyaluronic Acid Injection for Knee Osteoarthritis: A Synovial Membrane Investigation with Ultrasound Imaging
Bibliographic record
Abstract
We investigated whether hyaluronic acid (HA) injections can ameliorate ultrasound-detected synovitis in knee osteoarthritis (OA). We recruited 103 patients with symptomatic knee OA and ultrasound-detected synovitis and performed two ultrasound-guided fluid drainage procedures, followed by the administration of a low-molecular-weight HA injection (2.5 mL) in the subpatellar bursa, at a 2-week interval. Knee ultrasound imaging evaluations were performed before injection (baseline) and at 1 and 6 months after the second injection and included the measurements of synovial vascularity by using color Doppler ultrasound, synovial fluid depth over the suprapatellar bursa (SF), and synovial hypertrophy (SH). Initial clinical assessments included a visual analog scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). VAS scores decreased significantly at both 1-month and 6-month evaluations (p < 0.001). WOMAC scores also significantly decreased at 1 month (p < 0.001), but not at 6 months (p = 0.23). The ultrasound parameters did not significantly change, except color Doppler grading, which tended to decrease at the 6-month evaluation (p = 0.059). Our findings revealed that two ultrasound-guided HA injections following fluid drainage improved pain and knee function but did not considerably influence imaging-detected synovitis in patients with 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 | 0.001 | 0.003 |
| 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".