The Treatment of Trigeminal Neuralgia in Patients with Multiple Sclerosis using Percutaneous Radiofrequency Rhizotomy
Bibliographic record
Abstract
BACKGROUND: Trigeminal neuralgia (TN) has a higher incidence among patients with multiple sclerosis (MS) than in the general population. This cohort of MS patients with TN presents a series of management challenges including poor tolerance of antineuralgic medications and occasional bilateral presentation. We analyzed our surgical series of MS patients presenting with TN who were treated with percutaneous radiofrequency rhizotomy to estimate the success, failure and recurrence rate of this procedure for those patients. METHODS: Surgical reports were retrospectively reviewed between the years 1996-2000. Patients with MS and TN who received a percutaneous rhizotomy during that time were included in the study and followed until the end of 2002. Data regarding age, sex, duration of MS and pain, response to medical treatment, pain distribution and surgical outcome were evaluated. RESULTS: There were thirteen patients with MS and medically refractory TN treated with percutaneous radiofrequency rhizotomy. The average age at diagnosis for MS was 41 with TN beginning an average of eight years later. Following rhizotomy, complete pain relief without the need for any medication was achieved in 81% of the patients. The addition of medications resulted in pain control in the remaining patients. During a mean follow-up period of 52 months, there was a 50% recurrence rate. There were no complications related to the procedure and the associated facial numbness was well-tolerated. CONCLUSIONS: Percutaneous radiofrequency rhizotomy is a safe and effective method for the treatment of TN in patients with MS. The unique susceptibility of this cohort to the side effects of antineuralgic medications may require early consideration of rhizotomy.
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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) | 0.000 | 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) | 0.001 | 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, 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".