Motor Cortex Stimulation for Refractory Trigeminal Pain: A Case Series from Atlantic Canada
Bibliographic record
Abstract
INTRODUCTION: Neuromodulation is emerging as a promising intervention for intractable pain syndromes. Despite heterogeneous outcomes in the literature, motor cortex stimulation (MCS) has proven effective in addressing chronic orofacial pain. Painful trigeminal neuropathy (PTN) is characterized by constant facial pain and somatosensory signs. Refractory cases, unresponsive to medical and surgical therapies, significantly impact quality of life and pose socioeconomic challenges. This article presents a retrospective case series of five MCS patients who were treated for refractory PTN in Atlantic Canada. METHODS: Since 2021, eight cases of refractory PTN from Atlantic Canada have been recruited. Demographic and clinical characteristics were collected, with primary and secondary outcomes respectively defined as a 50% Visual Analogue Scale (VAS) reduction and a 60% reduction in morphine equivalent dose per day (MED, mg/day) at 12 months post-operation. Pain intensity was assessed preoperatively and at 6- and 12-month follow-ups, along with eating habits, return to work, and global satisfaction. RESULTS: Five patients with refractory PTN were implanted with MCS, all of whom achieved the primary and secondary outcomes. Preoperatively, mean VAS was 8.4 ± 1.0, reduced to 2.0 ± 1.1 at 12 months post-MCS, corresponding to a 77 ± 13% pain reduction. Average MED/day was reduced by 93 ± 13%, with 60% of participants narcotic-free at 12 months and 80% at 18 months. There were no immediate or delayed complications related to the MCS procedure. CONCLUSION: MCS exhibits significant potential for long-term pain relief and reducing opioid consumption in refractory trigeminal pain, emphasizing the necessity for large national multicenter trials.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".