Development of an Educational Curriculum for Implanting and Managing Vagus Nerve Stimulators for Epilepsy
Bibliographic record
Abstract
BACKGROUND: Vagus nerve stimulation (VNS) devices are commonly used for extracranial neuromodulation of drug-resistant epilepsy. These devices are implanted by multiple surgical subspecialties and managed by practitioners with varying levels of epilepsy-specific expertise. The North American Neuromodulation Society (NANS) education committee presents a curriculum defining level-dependent recommendations within the six-core competency rubric for the implantation and management of VNS devices. MATERIAL AND METHODS: A multidisciplinary (anesthesiology, neurology, neurosurgery, and physiatrists) and diverse (advanced practice providers, physicians, and surgeons) subcommittee of the NANS education committee met virtually over a year to develop a curriculum following the Accreditation Council for Graduate Medical Education (ACGME) core competencies. The subcommittee used a consensus approach, evidence-based development strategy; once completed, the VNS curriculum was approved by the NANS board. RESULTS: The subcommittee developed a VNS curriculum as a standard to be used for implanting surgeons, managing physicians, and advanced practice providers. The vertical orientation of the curriculum uses the ACGME educational core competencies framework; within this paradigm is a horizontal progression of skills with distinct competency groups for implanting surgeons and/or managing physicians. The horizontal progression defines the expected competence for early learner, advanced learner, and independent practitioner. CONCLUSION: A NANS education subcommittee iteratively developed a VNS curriculum for defining progressive competence of myriad care providers, including clinicians and advanced practice providers, within the ACGME six core competencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".