Radiofrequency Ablation of the Infrapatellar Branch of the Saphenous Nerve for the Treatment of Chronic Anterior Inferomedial Knee Pain
Bibliographic record
Abstract
INTRO: Genicular nerve radiofrequency ablation (GNRFA) is an effective treatment for chronic knee pain related to osteoarthritis. It is often utilized when conservative management has failed and patients wish to avoid arthroplasty, are poor surgical candidates due to comorbid medical conditions, or in those suffering from persistent pain after arthroplasty. The classic targets for GNRFA include the superior lateral genicular nerve, superior medial genicular nerve, and inferior medial genicular nerve but multiple anatomic studies have demonstrated additional sensory innervation to the knee. OBJECTIVE: In this research article, we propose an image-guided technique that can safely target the infrapatellar branch of the saphenous nerve which also provides sensory innervation to the anterior capsule. PROPOSAL: The proposed technique includes variations for conventional bipolar radiofrequency ablation, cooled radiofrequency ablation, dual-tined bipolar radiofrequency ablation, and monopolar radiofrequency ablation using a long axis approach. The described technique is based on updated anatomic studies and takes into account safety concerns such as thermal risk to the skin and/or pes anserine tendons and breaching of the synovial cavity. CONCLUSION: Future clinical research should be performed to confirm the safety and effectiveness of this specific approach.
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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 | 0.000 |
| 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".