Brainstem trigeminal fiber microstructural abnormalities are associated with treatment response across subtypes of trigeminal neuralgia
Bibliographic record
Abstract
ABSTRACT: Neurosurgical treatments for trigeminal neuralgia (TN) can provide long-lasting pain relief; however, some patients fail to respond and undergo multiple, repeat procedures. Surgical outcomes can vary depending on the type of TN, but the reasons for this are not well understood. Neuroimaging studies of TN point to abnormalities in the brainstem trigeminal fibers; however, whether this is a common characteristic of treatment nonresponse across different subtypes of TN is unknown. Here, we used diffusion tensor imaging (DTI) to determine whether the brainstem trigeminal fiber microstructure is a common biomarker of surgical response in TN and whether the extent of these abnormalities is associated with the likelihood of response across subtypes of TN. We studied 98 patients with TN (61 classical TN, 26 TN secondary to multiple sclerosis, and 11 TN associated with a solitary pontine lesion) who underwent neurosurgical treatment and 50 healthy controls. We assessed treatment response using pain intensity measures and examined microstructural features by extracting pretreatment DTI metrics from the proximal pontine segment of the trigeminal nerves. We found that microstructural abnormalities in the affected pontine trigeminal fibers (notably, lower fractional anisotropy and higher radial diffusivity) highlight treatment nonresponders (n = 47) compared with responders (n = 51) and controls, and that the degree of abnormalities is associated with the likelihood of surgical response across subtypes of TN. These novel findings demonstrate the value of DTI as an objective, noninvasive tool for the prediction of treatment response and elucidate the features that distinguish treatment responders from nonresponders in the TN population.
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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 | 0.002 |
| 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".