Successful use of electrosurgery in an occipitocervical fusion procedure in a patient with an established cochlear implant: illustrative case
Bibliographic record
Abstract
BACKGROUND: Neurotechnology is rapidly evolving, challenging surgeons to expand their expertise in managing patients with implanted devices. More than 700,000 persons use cochlear implants. Many others have implanted pacemakers and neuromodulation devices. Understanding electrosurgical interactions is critical for patient safety, yet the literature on this remains limited. Conventional electrosurgery, which uses high-frequency alternating current for hemostasis, is contraindicated in cochlear implant users due to the risk of electromagnetic interference (EMI). EMI can cause heating, component malfunction, or device failure. Despite shielding, induction currents and voltage surges may exceed device tolerance, posing risks. OBSERVATIONS: While PlasmaBlade safety is documented in cardiac surgery, its use in neurosurgery is underreported. The authors present a case demonstrating its safe application during an occipitocervical fusion in a 17-year-old with an established cochlear implant. The PlasmaBlade enabled exposure without affecting implant integrity or auditory function. LESSONS: As neurotechnology becomes increasingly prevalent, using innovative safety strategies, including monitoring electromagnetic fields, and adopting refined technologies like the PlasmaBlade will be impactful. These advancements have the potential to improve patient outcomes and ensure safer care for individuals with implanted devices. https://thejns.org/doi/10.3171/CASE25167.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".