Quality Improvement in Deep Brain Stimulation for Movement Disorders: Pandemic Impact on Specialized Elective Surgery
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is an important treatment for Parkinson's disease, tremor and dystonia in appropriately selected patients. The Canada Health Act emphasizes equity and "reasonable access to medically necessary hospital and physician services." How to define "reasonable access" has not been well studied. We aimed to assess access to DBS implantation surgery and to determine the time required from initial assessment through to surgery and which step(s) delay the implantation. METHODS: DBS implants from 2016 to 2023 at the University of Alberta were analyzed. The neurologists' decision to proceed with DBS marks the start of the workup. The time required to see a neurosurgeon, psychiatrist, neuropsychologist and healthcare allies and to receive DBS surgery was assessed. The impact of COVID-19 was studied. RESULTS: The total time from starting the workup to DBS surgery was 387.76 ± 125.19 days prior to COVID-19, and marked delay occurred during and post-COVID-19 (840.15 ± 165.41 days and 839.78 ± 300.66 days, respectively). Most workups were done within 6 months pre-COVID-19, although a big range existed due to variable factors. The longest delay to surgery was from consent to DBS implantation, owing to a lack of operative time. There has not been a recovery post-pandemic. CONCLUSIONS: Time to DBS implantation surgery from initial decision is lengthy and more than doubled over the course of the COVID-19 pandemic. The biggest delay was in the time from consent to implantation surgery, which has not improved despite the pandemic having ended.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".