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 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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".