Effect of Ultrasound-Guided Genicular Nerve Block in Knee Osteoarthritis with Neuropathic Pain
Bibliographic record
Abstract
Introduction: Osteoarthritis(OA) remains one of the most common musculoskeletal disorders. It was thought to be a non-inflammatory, wear and tear disorder, but recent studies have suggested the presence of a neuropathic pain component. Genicular nerve block has emerged as a new intervention to manage the neuropathic component of pain in knee OA. Materials and Methods: We conducted a prospective, hospital-based descriptive interventional study. Persons having OA with neuropathic pain component were identified using PainDETECT scale. We randomised the participants meeting the inclusion criteria into two groups. In the study group with thirty participants, we performed an ultrasound-guided triple-site genicular nerve block. The control group of equal size underwent conservative management. Participants were assessed using PainDETECT scale, Visual analogue scale (VAS) and Western Ontario McMaster Universities Arthritis Index (WOMAC) at baseline, 2 weeks and 4 weeks. Results: In the study group, PainDETECT score decreased from 24.93±1.99 at baseline to 8.07±2.97 at 2weeks and 7.9±2.87 at 4weeks. WOMAC significantly improved in the study group from 94.27±8.35 to 48.43±10.14 at 4weeks. VAS score also decreased in the study group from 9.2±0.71 to 4.73±1.44 at 2weeks and 4.53±1.28 at 4weeks. The control group also showed a significant decrease in PainDETECT score from 24.13±1.17 to 12.07±1.36 at 4weeks. However, a decrease in VAS score(from 9.13±0.68 to 7.67±0.67 at 4weeks) and WOMAC(from 97±4.49 to 88.5±4.93 at 4weeks) was less significant. Conclusion: Genicular nerve block provides significant pain relief and improved functional outcome in OA knee with neuropathic pain component. Furthermore, it is more effective than conservative management.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".