Continuous <scp>EEG</scp> monitoring: A survey of neurophysiologists and neurointensivists
Bibliographic record
Abstract
OBJECTIVE: Continuous EEG monitoring (cEEG) of critically ill adults is being used with increasing frequency, and practice guidelines on indications for cEEG monitoring have recently been published. However, data describing the current practice of cEEG in critically ill adults is limited. We aimed to describe the current practice of cEEG monitoring in adults in the United States. METHODS: A survey assessing cEEG indications and procedures was sent to one intensivist and one neurophysiologist responsible for intensive care unit (ICU) cEEG at 151 institutions in the United States. At some institutions only one physician could be identified. RESULTS: One hundred thirty-seven physicians from 97 institutions completed the survey. Continuous EEG is utilized by nearly all respondents to detect nonconvulsive seizures (NCS) in patients with altered mental status following clinical seizures, intra cerebral hemorrhage (ICH), traumatic brain injury, and cardiac arrest, as well as to characterize abnormal movements suspected to be seizures. The majority of physicians monitor comatose patients for 24-48 h. In an ideal situation with unlimited resources, 18% of respondents would increase cEEG duration. Eighty-six percent of institutions have an on-call EEG technologist available 24/7 for new patient hookups, but only 26% have technologists available 24/7 in-house. There is substantial variability in who reviews EEG and how frequently it is reviewed as well as use of quantitative EEG. SIGNIFICANCE: Although there is general agreement regarding the indications for ICU cEEG, there is substantial interinstitutional variability in how the procedure is performed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".