Reliability of EEG Reactivity in the Assessment of Comatose Patients Under Standardized Protocol (4692)
Bibliographic record
Abstract
Objective: To test the use of a standardized protocol for introducing afferent stimuli during electroencephalography (EEG) testing in critically ill comatose patients in the intensive care unit (ICU). Background: EEG provides clinical information about the state of comatose patients through the analysis of background rhythms and epileptiform discharges. This can also provide prognostic information, which can be augmented with the introduction of afferent stimuli and an analysis of changes in background rhythms associated with those stimuli. However, reactivity testing is not standardized and, thus, is often done non-uniformly between and within institutions. Design/Methods: A standardized reactivity protocol was implemented at London Health Sciences Centre. Specific stimuli (calling name, clap, nasal tickle, central pain, and central stimulation) were introduced at specific time-points during routine clinical EEG testing on comatose patients in the ICU. In this retrospective observational study, the first 50 sequential EEGs after implementation were reviewed with REB approval by 2 electroencephalographers and compared with clinical outcome. Inter-rater reliability was assessed. Results: Approximately 62% of patients tested demonstrated reactivity to a stimulus; approximately 48% showed reactivity to the first presented stimulus (calling name). Absence of reactivity was associated with poor outcome; whereas, presence of reactivity was not related to outcome. Inter-rater reliability for the different stimuli ranged from a Kappa statistic of 0.0557 (slight) to 0.4077 (moderate) for different stimuli. Conclusions: A standardized EEG protocol for introducing afferent stimuli to comatose patients during reactivity testing is feasible to implement. Although lack of reactivity was associated with poor outcome, this may reflect a ‘self-fulfilling prophecy’. This standardized protocol is well suited for further assessment by quantitative analysis to determine whether this could improve upon what is currently a subjective test. This protocol is also suited for use with other testing strategies to advance multi-modality testing for prognosis in critically ill comatose patients. Disclosure: Dr. Debicki has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Sunovion, UCB. Dr. Khateeb has nothing to disclose. Dr. Norton has nothing to disclose. Dr. Gofton has nothing to disclose.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".