P.129 Radiofrequency rhizotomy for trigeminal neuralgia under general anaesthetic with intraoperative neuromonitoring
Bibliographic record
Abstract
Background: Radiofrequency rhizotomy is an efficacious technique for treatment of trigeminal neuralgia that is classically performed with the patient awake. Previous studies have investigated methods for both anatomic and neurophysiologic optimization for nerve targeting. Methods: We performed a retrospective review of prospectively collected data on patients undergoing radiofrequency rhizotomy under a general anesthetic. Electrodes are placed in the temporalis, masseter and one of mylohyoid or anterior belly of digastric muscles. We then localize of the correct subdivision of the trigeminal nerve. The division of the trigeminal nerve with pain shows a muscle response, which is not present in normal subdivisions. Results: A total of 23 radiofrequency rhizotomies were performed under general anesthetic. Abnormal conduction reflexes were present in all cases, and dissipated after lesioning. Pre-operative BNI pain scores were 4.1 ± 0.3, which dropped to 1.8 ± 1.9 post-op (p=0.003). Number of pain medications (2.9 ± 0.6 v. 1.3 ± 1.3, p=0.007) and number of patients with opioid usage (78.7% v. 21.7%, p=0.007) both decreased after rhizotomy. Conclusions: Using a novel conduction pathway, we have successfully been able to monitor radiofrequency rhizotomy in patients operated on under a general anesthetic, with promising preliminary results. Further work is needed to better evaluate this intervention.
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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) | 0.007 | 0.003 |
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".