Gamma Knife Stereotactic Radiosurgery for Trigeminal Neuralgia Secondary to Multiple Sclerosis: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: The efficacy of stereotactic radiosurgery (SRS) for the relief of trigeminal neuralgia (TN) is well established. Much less is known, however, about the benefit of SRS for multiple sclerosis (MS)-related TN (MS-TN). OBJECTIVE: To compare outcomes in patients who underwent SRS for MS-TN vs classical/idiopathic TN and identify relative risk factors for failure. METHODS: We conducted a retrospective, case-control study of patients who underwent Gamma Knife radiosurgery at our center for MS-TN between October 2004 and November 2017. Cases were matched 1:1 to controls using a propensity score predicting MS probability using pretreatment variables. The final cohort consisted of 154 patients (77 cases and 77 controls). Baseline demographics, pain characteristics, and MRI features were collected before treatment. Pain evolution and complications were obtained at follow-up. Outcomes were analyzed using the Kaplan-Meir estimator and Cox regressions. RESULTS: There was no statistically significant difference between both groups with regards to initial pain relief (modified Barrow National Institute IIIa or less), which was achieved in 77% of patients with MS and 69% of controls. In responders, 78% of patients with MS and 52% of controls eventually had recurrence. Pain recurred earlier in patients with MS (29 months) than in controls (75 months). Complications were similarly distributed in each group and consisted, in the MS group, of 3% of new bothersome facial hypoesthesia and 1% of new dysesthesia. CONCLUSION: SRS is a safe and effective modality to achieve pain freedom in MS-TN. However, pain relief is significantly less durable than in matched controls without MS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".