Failure of SCS MR-Conditional Modes Due to High Impedance: A Review of Literature and Case Series
Bibliographic record
Abstract
INTRODUCTION: Magnetic resonance imaging (MRI) conditional modes are a novel feature for certain Food and Drug Administration (FDA)-approved spinal cord stimulation (SCS) devices. However, there is a paucity of literature around the limitation of MRI-conditional modes ("MRI safe"), specifically in clinical scenarios where urgent MRIs may be needed. One such limitation is load impedance, referring to the circuit's resistance to the current being generated by the system. High impedance can limit the MRI-conditional mode capability, presenting potential harm to a patient undergoing an MRI or make an MRI unable to be completed. METHODS: Three cases were identified, and informed consent was obtained. All information was obtained via retrospective chart review. RESULTS: In this case series of three patients where MRI-conditional SCS systems were unable to be placed in "MRI safe" settings, preventing timely MRI study completion in the setting of high impedance, all three were required to undergo alternative imaging including CT scans, and two patients ultimately had the system explanted and one chose to be re-implanted after completion of scans. CONCLUSION: This case series highlights the need for further investigation of impedance in SCS systems and potential limitations for future MRI usage. The review of literature of impedance in SCS shows both device- and physiologic-related etiologies for changes in impedance that warrant consideration by the implanting physician.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".