Trigeminal neuralgia associated with multiple sclerosis: A multimodal assessment of brainstem plaques and response to Gamma Knife radiosurgery
Bibliographic record
Abstract
BACKGROUND: Gamma Knife radiosurgery (GKRS) is a minimally invasive procedure for trigeminal neuralgia secondary to multiple sclerosis (MS-TN). Patients with MS-TN experience suboptimal response rates to treatment, and the relationship between trigeminal microstructure and treatment outcome is poorly understood. OBJECTIVE: To characterize imaging features of MS-TN pain and GKRS response. METHODS: 3 T diffusion-weighted imaging (DWI), T1-w, T2-w, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired for 18 MS-TN patients undergoing GKRS. Brainstem plaques were standardized into a common space to determine plaque distribution. Ratio of T1-w/T2-w or "myelin maps (MM)" was generated. Multi-tensor tractography was used to delineate the radiosurgical target (RT), root entry zone (REZ), and proximal pontine segment (PPS) of the trigeminal nerves. RESULTS: Laterality of MS-TN is associated with increased axial diffusivity at the PPS, whereas decreased MM at the PPS correlated with poor GKRS response. Preoperatively, GKRS responders have higher fractional anisotropy at the RT, higher axial diffusivity at the REZ, and higher MM intensities at the PPS. CONCLUSION: This study demonstrates that diffusivities and MM intensities are important correlates of pain and treatment response, respectively. Overall, preoperative multimodal assessment of the central trigeminal pathway is a better indicator of GKRS response than postoperative assessment of the reduction in fractional anisotropy peripherally.
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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 | 0.001 | 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".