The American Clinical Neurophysiology Society Guideline on Indications for Continuous Electroencephalography Monitoring in Neonates
Bibliographic record
Abstract
PURPOSE: Continuous EEG (cEEG) monitoring is increasingly used in the management of neonates with seizures. There remains debate on what clinically relevant information can be gained from cEEG in neonates with suspected seizures, at high risk for seizures, or with definite seizures, as well as the use of cEEG for prognosis in a variety of conditions. In this guideline, we address these questions using American Clinical Neurophysiology Society structured methodology for clinical guideline development. METHODS: A working group was formed from American Clinical Neurophysiology Society membership with expertise in neonatal cEEG and a set of priority questions developed. We performed literature searches in PubMed and EMBASE to identify relevant studies. Evidence tables were compiled from extracted data and quality assessments performed. A modification of the GRADE process was used to evaluate the body of evidence and draft recommendations. RESULTS: Our working group identified six priority questions to evaluate the accuracy of cEEG for neonatal seizure diagnosis and the formulation of prognosis. An initial literature search yielded 18,167 results, which were distilled to a set of 217 articles. Overall, the quality of evidence for most priority questions was rated as very low and we provided conditional recommendations based on published literature and expert consensus. For each priority question, we also considered the benefits and harms of cEEG, with relative harms considered to be far less than the potential benefits across recommendations. CONCLUSIONS: We present evidence-based clinical guidelines regarding indications for cEEG monitoring in neonates. Considering resource utilization and feasibility, when cEEG monitoring results have a likelihood of altering clinical decision making, the authors felt the resource investment was justifiable.
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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 | 0.026 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".