CyberKnife radiosurgery for trigeminal neuralgia: a retrospective review of 168 cases
Bibliographic record
Abstract
OBJECTIVE: Gamma Knife radiosurgery is recognized as an efficient intervention for the treatment of refractory trigeminal neuralgia (TN). The CyberKnife, a more recent frameless and nonisocentric radiosurgery alternative, has not been studied as extensively for this condition. This study aims to evaluate the clinical outcomes of a first CyberKnife radiosurgery (CKRS) treatment in patients with medically refractory TN. METHODS: A retrospective cohort study of 166 patients (168 procedures) with refractory TN treated from 2009 to 2021 at the Centre Hospitalier de l'Université de Montréal was conducted. The treatment was performed using a CyberKnife (model G4, VSI, or M6). The treatment median maximum dose was 80 (range 70.0-88.9) Gy. RESULTS: Adequate pain relief, evaluated using Barrow Neurological Institute pain scale scores (I-IIIb), was achieved in 146 cases (86.9%). The median latency period before adequate pain relief was 35 (range 0-202) days. The median duration of pain relief for cases with a recurrence of pain was 8.3 (range 0.6-85.0) months. The actuarial rates of maintaining adequate pain relief at 12, 36, and 60 months from the treatment date were 77.0%, 62.5%, and 50.2%, respectively. There was new onset or aggravation of facial numbness in 44 cases (26.2%). This facial numbness was predictive of better maintenance of pain relief (p < 0.001). The maintenance of adequate pain relief was sustained longer in idiopathic cases compared with cases associated with multiple sclerosis (MS; p < 0.001). CONCLUSIONS: In the authors' experience, CKRS for refractory TN is efficient and safe. The onset or aggravation of facial hypoesthesia after treatment was predictive of a more sustained pain relief, and idiopathic cases had more sustained pain relief in comparison with MS-related cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".