Comprehensive Needs Assessment for Deep Brain Stimulation in Canada, A Health Service Research Perspective
Bibliographic record
Abstract
BACKGROUND: The Canadian healthcare system is subject to national standards that may be challenging to meet, given the evolution and integration of technology in healthcare in disciplines like functional neurosurgery, utilizing therapies such as deep brain stimulation (DBS), whereby implanted devices have provided benefit for patients with movement disorders. A comprehensive assessment of the need for this service to match with the delivery of DBS has not been performed. This thesis comprises a series of studies that aim to address this knowledge gap through the quadruple aim of health service research. METHODS: The first study is a systematic review and meta-analysis including economic evaluations comparing DBS for movement disorders with medical management only. The second is a mixed methods survey of Canadian stakeholders for DBS. The final study is a nationwide retrospective cohort study of DBS patients from 2019-2022 to determine factors that may influence access. RESULTS: Through analysis of 14 economic evaluations, DBS appears to be a cost-effective treatment when considered across the remaining lifespan of the patient with positive incremental net benefit for DBS with a mean difference of 40,504.81USD (95% CI 2,422.42; 78,587.19). Additionally, 220 responses from all DBS stakeholder groups revealed that costs associated with travel, waitlists, lack of specific resources, poor understanding of movement disorders and DBS indications, and referral pathways were barriers to accessing DBS. Finally, preliminary results identified 162 DBS patients. Potential factors that may increase access to DBS were indication (Parkinson’s disease), higher socioeconomic status, and race. CONCLUSIONS: While DBS is a cost-effective therapy for patients with movement disorders, the current delivery of this service needs significant improvement. This includes improved education, streamlined referral pathways, and policy change at a governmental level, with further investigation to determine regions of the country where need for DBS far exceeds current access.
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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 | 0.037 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".