Deep Brain Stimulation Surgery Programs in Canada
Bibliographic record
Abstract
Deep brain stimulation involves the surgical insertion of electrodes to stimulate targeted areas of the brain. It is recommended to help control movement-related symptoms of Parkinson disease with certain indications and contraindications to consider. This rapid Environmental Scan describes the landscape of deep brain stimulation surgery across Canada and identifies conditions other than Parkinson disease that can benefit from the therapy. It also provides an overview of cost-effectiveness studies on deep brain stimulation for Parkinson disease. Emerging indications for deep brain stimulation include refractory obsessive-compulsive disorder, refractory epilepsy, treatment-resistant Tourette syndrome, certain types of pain, refractory major depressive disorder, tardive dyskinesia, and essential tremor. In Canada, there are deep brain stimulation surgery programs in Alberta, British Columbia, Manitoba, Nova Scotia, Ontario, Quebec, and Saskatchewan. The number of qualified neurosurgeons for deep brain stimulation surgery ranges from 1 to 5 (at least) across jurisdictions. Overall, deep brain stimulation is considered cost-effective for people living with advanced Parkinson disease. The risk of developing Parkinson disease increases with age, with onset typically occurring in late adulthood. The number of people eligible for deep brain stimulation in Canada is expected to increase with the aging population and emerging indications. Information related to existing surgery programs can help support capacity planning for deep brain stimulation surgery in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".