Trigeminal nerve and white matter brain abnormalities in chronic orofacial pain disorders
Bibliographic record
Abstract
The orofacial region is psychologically important, given that it serves fundamental and important biological purposes. Chronic orofacial pain disorders affect the head and neck region. Although some have clear peripheral etiologies, eg, classic trigeminal neuralgia, others do not have a clear etiology (eg, muscular temporomandibular disorders). However, these disorders provide a unique opportunity in terms of elucidating the neural mechanisms of these chronic pain conditions: both the peripheral and central nervous systems can be simultaneously imaged. Diffusion-weighted imaging and diffusion tensor imaging have provided a method to essentially perform in vivo white matter dissections in humans, and to elucidate abnormal structure related to clinical correlates in disorders, such as chronic orofacial pains. Notably, the trigeminal nerve anatomy and architecture can be captured using diffusion imaging. Here, we review the trigeminal somatosensory pathways, diffusion-weighted imaging methods, and how these have contributed to our understanding of the neural mechanisms of chronic pain disorders affecting the trigeminal system. We also discuss novel findings indicating the potential for trigeminal nerve diffusion imaging to develop diagnostic and precision medicine biomarkers for trigeminal neuralgia. In sum, diffusion imaging serves both an important basic science purpose in identifying pain mechanisms, but is also a clinically powerful tool that can be used to improve treatment outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".