Effectiveness of Ultrasound-guided versus Anatomical Landmark-guided Genicular Nerve Block to Treat Chronic Knee Osteoarthritis: A Retrospective Cohort Study
Bibliographic record
Abstract
Objectives: There is limited data on the relative effectiveness of different techniques used for administering genicular nerve block (GNB) for pain management of chronic knee osteoarthritis (OA) in the Malaysian population. This study aims to determine and compare the effectiveness of GNB administered using two pain management techniques—anatomical landmark-guided (ALG) and ultrasound-guided (USG)—for chronic knee OA in this population. Methods: This retrospective cohort study included 40 patients with chronic knee OA who received GNB, 20 of whom underwent treatment with the USG technique and the other 20 with the ALG technique. Pain, stiffness, and functional limitation scores were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index Questionnaire (WOMAC) and Numeric Rating Scale (NRS-11) at baseline and post-treatment day one, three weeks, and six weeks. Results: Both groups reported a significant reduction in WOMAC and NRS-11 scores as per their feedback on day one, three weeks, and six weeks post-treatment. Greater reductions in WOMAC and NRS-11 scores were reported by patients who received GNB via USG than by ALG technique, the difference achieving statistical significance at six weeks after treatment (p =0.026). Conclusions: GNB administration using USG and ALG techniques are both effective in significantly reducing pain, stiffness, and functional limitation in patients suffering from chronic knee OA. Among the two techniques, USG appears to be more effective. Nevertheless, GNB guided by ALG continues to be a viable treatment modality, especially in healthcare settings with limited to no USG facilities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".