Nonawake versus Awake Placement of Spinal Cord Stimulators in Canada
Bibliographic record
Abstract
BACKGROUND: Spinal cord stimulation (SCS) is a common therapeutic approach for treating intractable chronic pain. A key factor determining SCS efficacy is lead positioning to generate paresthesias in areas of perceived pain. There are two distinct approaches to confirming appropriate coverage. 1) Sedative anesthesia with local anesthetic and intraoperative patient reporting of pain coverage. 2) General anesthesia and intraoperative neurophysiological mapping. Placement guided by neuromonitoring decreases OR times, produces more accurate placement with better pain coverage, less excess paresthesias and adverse events. We aim to determine the prevalence of non-awake SCS placement with neuromonitoring in Canada, given the demonstrated benefits, and to identify possible barriers to implementation. METHODS: A structured questionnaire was designed to assess procedures for SCS implantation in Canada. The survey was distributed via email to members of the Canadian Neuromodulation Society. RESULTS: 14 responses were received. 36% perform SCS implantation asleep with neuromonitoring where 75% utilize CMAPs and 25% utilize SSEP collisions. 71% have access to a neurophysiologist yet 93% are at centres where neurophysiologists are used for other procedures. Barriers to utilizing neurophysiologist assisted lead placement include familiarity with the awake procedure, and lack of access and awareness. CONCLUSION: This survey provides a summary of SCS implantation practice patterns in Canada. Although asleep SCS implantation with neuromonitoring is faster and results in more accurate placement while avoiding downsides of the awake procedure, most neurosurgeons currently do not utilize this protocol in part due to a lack of access to neurophysiologists with expertise in this area.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".