Microsurgical rhizotomy for trigeminal neuralgia in MS patients: technique, patient satisfaction, and clinical outcomes
Bibliographic record
Abstract
OBJECTIVE: Patients with multiple sclerosis (MS)-associated trigeminal neuralgia (TN) have higher recurrence and retreatment rates than non-MS patients. The optimal management strategy and role for microsurgical rhizotomy (MSR) for MS-TN remains to be determined. The aim of this study was to report time to treatment failure (TTF) and pain scores following MSR compared to percutaneous and Gamma Knife procedures. METHODS: Time to treatment failure was analyzed after MSR (n = 14) versus prior procedures (n = 53) among MS-TN patients. Kaplan-Meier curves and log-rank test were utilized to compare TTF after MSR versus prior procedures using the same cohort of patients as their own control group. Subsequent analysis compared TTF after MSR to TTF after 93 other procedures among a second cohort of 18 MS-TN patients not undergoing MSR. BNI pain scores were compared between MSR and other procedures among the MS-TN cohort using a chi-square test. RESULTS: TTF was significantly longer after MSR than after other procedures in the MSR cohort (median TTF 79 vs 10 months, respectively, p < 0.0001). Similarly, TTF was longer after MSR than after prior procedures in the non-MSR cohort (median TTF 79 vs 13 months, respectively, p < 0.001). MSR resulted in a higher proportion of excellent pain scores when compared to other procedures in the non-MSR cohort (77% vs 29%, p < 0.001). Probability of treatment survival was higher after MSR than after other procedures at all time points (3, 6, 12, 24, 36, and 48 months). There were no deaths or major complications after MSR. CONCLUSIONS: TTF was significantly longer following MSR compared to prior procedures in MS-TN patients. Additionally, a higher proportion of patients achieved excellent BNI pain scores after MSR.
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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 | 0.001 | 0.003 |
| 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".