Establishing a Standard of Care for Deep Brain Stimulation Centers in Canada
Bibliographic record
Abstract
During the "DBS Canada Day" symposium held in Toronto July 4-5, 2014, the scientific committee invited experts to share their knowledge regarding deep brain stimulation (DBS) management of movement disorders in three domains: (1) the programming algorithms, (2) the necessary team to run a neurosurgery program, and (3) the appropriate scales to better define in a more comprehensive fashion the effect of the brain surgery. Each presentation was followed by an open discussion, and this article reports on the conclusions of this meeting on these three questions. Concerning programming, the role of the pulse width and the switching off of the stimulation at night for thalamic stimulation for the control of tremor have been discussed. The algorithms proposed in the literature for programming in Parkinson's disease (PD) need validation. In dystonia, the use of monopolar vs bipolar parameters, the use of low vs high frequencies and the use of smaller versus larger pulse widths all need to be examined properly. Concerning the necessary team to run a neurosurgical program, recommendations will follow the suggestions for standardized outcome measures. Regarding the outcome measures for DBS in PD, investigations need to focus on the non-motor aspects of PD. Identifying which nonmotor symptoms respond to DBS would allow a better screening before and satisfaction postoperatively. There is an important need for more data to determine the optimal programming protocol and the standard measures that should be performed routinely by all centers.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".