P.126 Cyberknife radiosurgery for trigeminal neuralgia: a retrospective review of 168 cases
Bibliographic record
Abstract
Background: Gamma Knife radiosurgery for the treatment of refractory trigeminal neuralgia is recognized as an efficient intervention. The CyberKnife, a more recent frameless radiosurgery alternative, has not been studied as extensively for this condition. The aim of this study is to evaluate the clinical outcomes of a first CyberKnife radiosurgery treatment on patients with medically refractory trigeminal neuralgia. Methods: A retrospective study of 166 patients (168 cases) with refractory trigeminal neuralgia treated since 2009 with CyberKnife radiosurgery at the Centre Hospitalier de l’Université de Montreal (CHUM). Results: Adequate pain relief (Barrow Neurological Institute (BNI) pain scores I-IIIb) was achieved in 146 cases (86.9%). The median latency period before adequate pain relief was 35 days (range 0-202 days). The median duration of adequate pain relief was 15.8 months (range 0.6-85.0[DR1] [AG2] [AG3] [AG4] months). The actuarial rates of maintenance of adequate pain relief at 12, 36, and 60 months were 77.0%, 62.5%, and 50.2%, respectively. There was a new-onset or aggravation of facial numbness in 44 cases (26.2%). The maintenance of an adequate pain relief was more sustained in idiopathic cases in comparison to cases associated with multiple sclerosis (P< 0.001). Conclusions: In our experience CyberKnife radiosurgery for refractory trigeminal neuralgia is efficacious and safe.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".