Predictive Value of Synovial Hypertrophy and Synovial Effusion on Ultrasound for Clinical Outcomes of Genicular Nerve Ablation in Knee Osteoarthritis
Bibliographic record
Abstract
Abstract Background: Genicular nerve ablation (GNA) is an effective minimally invasive intervention for pain relief in knee osteoarthritis (OA). However, the predictive role of pre-procedural ultrasound findings, particularly synovial effusion (SE) and hypertrophy, remains underexplored. This study aimed to evaluate whether these sonographic parameters predict short-term outcomes following GNA. Materials and Methods: This observational study included 24 patients with chronic knee OA who underwent ultrasound-guided GNA targeting the superior medial, superior lateral, and inferior medial genicular nerves. SE and hypertrophy were graded according to Outcome Measures in Rheumatology and quantitative effusion thickness. Pain and function were assessed using the Numeric Rating Scale (NRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline and 4 weeks post-procedure. Patients achieving ≥50% NRS reduction were defined as responders. Predictive factors were analyzed using Firth logistic regression. Results: Mean NRS improved from 6.75 ± 0.99 to 3.88 ± 1.83 ( P < 0.001), and mean WOMAC improved from 38.5 ± 3.9 to 15.7 ± 13.7 ( P < 0.001). Patients with mild or no SE showed greater improvement (WOMAC reduction = 33 points) than those with moderate/severe effusion (18 points). Similarly, mild/no hypertrophy showed a larger WOMAC decrease (28 vs 19 points). Firth logistic regression identified synovial hypertrophy (SH) as an independent predictor of response (adjusted odds ratios (OR) = 8.75; 95% confidence intervals 1.86–41.10; P = 0.007), whereas effusion showed a nonsignificant trend (OR = 0.67; P = 0.47). Conclusion: GNA provides significant short-term improvement in pain and function in knee OA. Ultrasound assessment of the synovial morphology enhances prognostication—patients with minimal SH and effusion demonstrate the greatest likelihood of favourable outcomes. Incorporating pre-procedural ultrasound phenotyping into routine evaluation may optimize patient selection and clinical success of GNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".