Impact of Multiple Sclerosis Subtypes on Pain Management in Patients With Trigeminal Neuralgia After Stereotactic Radiosurgery: An International Multicenter Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Trigeminal neuralgia affects approximately 2% of patients with multiple sclerosis (MS) and often shows higher rates of pain recurrence after treatment. Previous studies on the effectiveness of stereotactic radiosurgery (SRS) for trigeminal neuralgia did not consider the different MS subtypes, including remitting relapsing (RRMS), primary progressive (PPMS), and secondary progressive (SPMS). Our objective was to investigate how MS subtypes are related to pain control (PC) rates after SRS. METHODS: We conducted a retrospective multicenter analysis of prospectively collected databases. Pain status was assessed using the Barrow National Institute Pain Intensity Scales. Time to recurrence was estimated through the Kaplan-Meier method and compared groups using log-rank tests. Logistic regression was used to calculate the odds ratio (OR). RESULTS: Two hundred and fifty-eight patients, 135 (52.4%) RRMS, 30 (11.6%) PPMS, and 93 (36%) SPMS, were included from 14 institutions. In total, 84.6% of patients achieved initial pain relief, with a median time of 1 month; 78.7% had some degree of pain recurrence with a median time of 10.2 months for RRMS, 8 months for PPMS, 8.1 months for SPMS ( P = .424). Achieving Barrow National Institute-I after SRS was a predictor for longer periods without recurrence ( P = .028). Analyzing PC at the last available follow-up and comparing with RRMS, PPMS was less likely to have PC (OR = 0.389; 95% CI 0.153-0.986; P = .047) and SPMS was more likely (OR = 2.0; 95% CI 0.967-4.136; P = .062). A subgroup of 149 patients did not have other procedures apart from SRS. The median times to recurrence in this group were 11.1, 9.8, and 19.6 months for RRMS, PPMS, and SPMS, respectively (log-rank, P = .045). CONCLUSION: This study is the first to investigate the relationship between MS subtypes and PC after SRS, and our results provide preliminary evidence that subtypes may influence pain outcomes, with PPMS posing the greatest challenge to pain management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".