Canadian Assessment of Deep Brain Stimulation Access: The Canada Study
Bibliographic record
Abstract
BACKGROUND: The Canada Health Act requires reasonable access to all medically necessary therapies. No information is available to assess the current access to neuromodulation across Canada. This study quantifies the current rate of deep brain stimulation (DBS) for the entire country of Canada. Analyses were performed to determine whether there were differences in access based on provincial or territorial location, rural or non-rural region, or socioeconomic status. METHODS: All implanted DBS devices in Canada over a 2-year epoch (January 2015 to December 2016) were supplied by either Boston Scientific or Medtronic. Investigators received anonymized data from these companies, including patient age and home residence region. The 2016 Statistics Canada census data were used to determine the rate of DBS surgery and whether access was related to provincial location, rural versus non-rural region or socioeconomic status. RESULTS: A total of 722 patients were studied. The rate of DBS surgery for the entire country was ten per million population per year. Saskatchewan was significantly above (374%) the national average, whereas Quebec (40%) and Newfoundland & Labrador (32%) were significantly below the national average. No patients from the three territories received DBS. There were no significant differences in access from rural versus non-rural areas or in regions within provinces with different socioeconomic status. CONCLUSIONS: This is the first study to quantify all patients receiving DBS within an entire country. The current rate of DBS surgery within Canada is ten cases per million per year. Statistically significant regional differences were discovered and discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".