Reversal of insular and microstructural nerve abnormalities following effective surgical treatment for trigeminal neuralgia
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Bibliographic record
Abstract
Classical trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder commonly associated with neurovascular compression at the trigeminal nerve root entry zone (REZ). Neurosurgical interventions can relieve TN pain, but the mechanisms underlying these effects are unknown. We determined whether the abnormalities we previously reported at the REZ of TN patients using diffusion tensor imaging (DTI) and brain gray matter (GM) analyses resolve after effective neurosurgical treatment. Twenty-five patients who underwent either microvascular decompression surgery or Gamma Knife radiosurgery for right-sided TN had magnetic resonance imaging scans before and after treatment and were compared with age-matched controls. Cortical thickness and voxel-based morphometry examined specific brain GM we previously reported as abnormal in TN. White matter metrics of fractional anisotropy (FA), mean, radial, and axial diffusivities (MD, RD, and AD, respectively) were extracted bilaterally from each trigeminal REZ. Before treatment, patients had widespread GM abnormalities including thinner ventral anterior insula (vAI) cortex, and REZ microstructural abnormalities (lower FA, and higher MD, RD, and AD) compared with controls. We considered a 75% reduction in pain as effective treatment. The right vAI was the only GM region that normalized toward the level of healthy controls after effective treatment. At the REZ, effective treatment reversed FA, MD, RD, and AD abnormalities and was correlated with pain relief after treatment. These results demonstrate that treatment can effectively resolve pain by normalizing REZ abnormalities, which may influence vAI abnormalities. Future studies should consider DTI as an adjunct to assess the patient outcome and subtle microstructural changes after treatment.
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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) | 0.000 | 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) | 0.000 | 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 it