P.053 Development of an EEG curriculum for icu nurses to facilitate real time screening of continuous EEG data for seizures in critically ill adults
Bibliographic record
Abstract
Background: Nonconvulsive seizures (NCSz) occur commonly in critically ill patients and are harmful. Diagnosis requires detection with continuous electroencephalography (cEEG) that necessitates frequent interpretation by experts. This is often not possible, and requires large amounts of resources. Screening level interpretation of cEEG by ICU nurses to facilitate timely expert diagnosis may be one solution. Methods: Kern’s approach to curriculum development was utilized to inform creation of a cEEG curriculum for ICU nurses. Results: A needs assessment revealed 80%, 94%, and 100% of nurses lacked comfort in basic seizure/EEG principles, EEG and CDSA interpretation respectively. The most requested method of learning (76%) involved simulation. A spiral curriculum of 15 interactive online tutorials with corresponding practice/simulation modules providing instant feedback was developed. To evaluate curriculum impact, time spent on modules, improvement in nursing knowledge, and diagnostic accuracy will be evaluated using pre and post curriculum tests. Participant satisfaction will be evaluated using electronic surveys. Conclusions: Development of a curriculum to teach ICU nurses basic screening diagnostic skills for NCSz is possible. Moving forward, we hope to refine and validate this learning tool and formally implement its use to help screen for NCSz prior to expert interpretation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".