F.09 Neurosurgical Outcomes in patients with Multiple Sclerosis related Trigeminal Neuralgia
Bibliographic record
Abstract
Background: The aim of this study was to assess the outcomes of surgery for multiple sclerosis-related trigeminal neuralgia (MS-TN). Methods: All Manitobans undergoing first surgery for medically refractory MS-TN between 2000 and 2014 were identified. The time interval until additional surgeries were required for recurrent pain, defined as the time to fail (TTF), was determined from a retrospective chart review. Kaplan-Meier analyses were performed and outcomes compared. Results: Twenty-one patients (26 sides) underwent first rhizotomy by GammaKnife (GK, 13), glycerol injection (PGR, 10) or balloon compression (BCR, 3). Second procedures were required in 88% at 15±13 months, including GK (24), PGR (19), BCR (25), microvascular decompression (2) and open surgical partial rhizotomy (Dandy, 4) for an overall total of 99 surgeries (1-12 per side). The additional GK, PGR, and BCR eventually failed and required further surgeries in 40%, 60% and 70% at 1, 2, and 3 years respectively with a trend to longer TTF compared to first surgeries (ns). Follow up of Dandy procedures, however, identified no pain recurrence at 4 to 110 months. Conclusions: The minimally invasive rhizotomies for MS-TN were associated with high rates of recurrence and reoperation. Long term pain relief was best achieved with a Dandy procedure, even after multiple prior rhizotomies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".