Seizure Identification by Critical Care Providers Using Quantitative Electroencephalography
Bibliographic record
Abstract
OBJECTIVES: To compare the performance of critical care providers with that of electroencephalography experts in identifying seizures using quantitative electroencephalography display tools. DESIGN: Diagnostic accuracy comparison among healthcare provider groups. SETTING: Multispecialty quaternary children's hospital in Canada. SUBJECTS: ICU fellows, ICU nurses, neurophysiologists, and electroencephalography technologists. INTERVENTION: Two-hour standardized one-on-one training, followed by a supervised individual review of 27 continuous electroencephalography recordings with the task of identifying individual seizures on eight-channel amplitude-integrated electroencephalography and color density spectral array displays. MEASUREMENTS AND MAIN RESULTS: Each participant reviewed 27 continuous electroencephalograms comprising 487 hours of recording containing a total of 553 seizures. Performance for seizure identification was compared among groups using a nested model analysis with adjustment for interparticipant variability within groups and collinearity among recordings. Using amplitude-integrated electroencephalography, sensitivity for seizure identification was comparable among ICU fellows (83.8%), ICU nurses (73.1%), and neurophysiologists (81.5%) but lower among electroencephalographic technologists (66.7%) (p = 0.003). Using color density spectral array, sensitivity was comparable among ICU fellows (82.4%), ICU nurses (88.2%), neurophysiologists (83.3%), and electroencephalographic technologists (73.3%) (p = 0.09). Daily false-positive rates were also comparable among ICU fellows (2.8 for amplitude-integrated electroencephalography, 7.7 for color density spectral array), ICU nurses (4.2, 7.1), neurophysiologists (1.2, 1.5), and electroencephalographic technologists (0, 0) (p = 0.41 for amplitude-integrated electroencephalography; p = 0.13 for color density spectral array). However, performance varied greatly across individual electroencephalogram recordings. Professional background generally played a greater role in determining performance than individual skill or electroencephalogram recording characteristics. CONCLUSIONS: Following standardized training, critical care providers and electroencephalography experts displayed similar performance for identifying individual seizures using both amplitude-integrated electroencephalography and color density spectral array displays. Although these quantitative electroencephalographic trends show promise as a tool for bedside seizure screening by critical care providers, these findings require confirmation in a real-world ICU environment and in daily clinical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".