Histopathological effects of radiosurgery on a human trigeminal nerve
Bibliographic record
Abstract
BACKGROUND: Radiosurgery is a well-established treatment modality for medically refractory trigeminal neuralgia. The exact mechanism of pain relief after radiosurgery is not clearly understood. Histopathology examination of the trigeminal nerve in humans after radiosurgery is rarely performed and has produced controversial results. CASE DESCRIPTION: We report on a 45-year-old female who received radiosurgery treatment for trigeminal neuralgia by Cyberknife. A 6-mm portion of the cisternal segment of trigeminal nerve received a dose of 60 Gy. The clinical benefit started 10 days after therapy and continued for 8 months prior to a recurrence of her previous symptoms associated with mild background pain. She underwent microvascular decompression and partial sensory root sectioning. Atrophied trigeminal nerve rootlets were grossly noted intraoperatively under surgical microscope associated with changes in trigeminal nerve color to gray. A biopsy from the inferolateral surface of the nerve proximal to the midcisternal segment showed histological changes in the form of fibrosis and axonal degeneration. CONCLUSION: This case study supports the evidence of histological damage of the trigeminal nerve fibers after radiosurgery therapy. Whether or not the presence and degree of nerve damage correlate with the degree of clinical benefit and side effects are not revealed by this study and need to be explored in future studies.
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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 | 0.000 |
| 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) | 0.002 | 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".