A comparative 3D anatomical simulation study of four techniques to ablate the infrapatellar branch of the saphenous nerve
Bibliographic record
Abstract
BACKGROUND: Image-guided radiofrequency ablation of the superior and inferior medial genicular nerves is used to treat medial knee pain. The infrapatellar branch (saphenous nerve) has been suggested as an additional nerve target. No studies have assessed nerve capture rates of the techniques. OBJECTIVE: To simulate four radiofrequency ablation techniques (cooled/long-axis/conventional bipolar strip lesion/dual-tined techniques) to (1) visualize the lesions in 3D relative to the treatment line and compare their extent; (2) determine and compare capture rates of the infrapatellar branch and inferior medial genicular nerve; and (3) assess which technique(s) would be most effective. DESIGN: Anatomical simulation study. METHODS: 3D models were reconstructed, based on previously collected data of the dissection/digitization of 7 specimens. Four techniques were simulated with lesion sizes obtained from previously published data or manufacturer's specifications. Capture rates of the infrapatellar branch and inferior medial genicular nerve were compared and the extent of the lesion relative to the treatment line was visualized. RESULTS: The cooled monopolar technique resulted in over 50% capture rate of the superior infrapatellar branch and anterior branch of inferior medial genicular nerve. This was followed by the dual-tined monopolar technique, capturing 42.9% of superior infrapatellar branch and 57.1% of anterior branch of inferior medial genicular nerve. The simulated lesions did not always encompass the treatment line inferiorly, sparing the inferior infrapatellar branch. All techniques resulted in complete sparing of the infrapatellar branch in some specimens. CONCLUSIONS: High-fidelity lesion simulation of radiofrequency ablation techniques provides a robust anatomical foundation to inform image-guided interventions for medial knee pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".