Guidelines for Qualifications of Neurodiagnostic Personnel: A Joint Position Statement of the American Clinical Neurophysiology Society, the American Association of Neuromuscular & Electrodiagnostic Medicine, the American Society of Neurophysiological Monitoring, and ASET – The Neurodiagnostic Society
Bibliographic record
Abstract
The Guidelines for Qualifications of Neurodiagnostic Personnel (QNP) document has been created through the collaboration of the American Clinical Neurophysiology Society (ACNS), the American Society of Neurophysiological Monitoring (ASNM), the American Association of Neuromuscular & Electrodiagnostic Medicine (AANEM), and ASET - The Neurodiagnostic Society (ASET). The quality of patient care is optimized when neurophysiological procedures are performed and interpreted by appropriately trained and qualified practitioners at every level. These Societies recognize that Neurodiagnostics is a large field with practitioners who have entered the field through a variety of training paths. This document suggests job titles, associated job responsibilities, and the recommended levels of education, certification, experience, and ongoing education appropriate for each job. This is important because of the growth and development of standardized training programs, board certifications, and continuing education in recent years. This document matches training, education, and credentials to the various tasks required for performing and interpreting Neurodiagnostic procedures. This document does not intend to restrict the practice of those already working in Neurodiagnostics. It represents recommendations of these Societies with the understanding that federal, state, and local regulations, as well as individual hospital bylaws, supersede these recommendations. As Neurodiagnostics is a growing and dynamic field, we fully intend this document to change over time.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.026 |
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".