P.068 Reliability of EEG reactivity in assessment of comatose patients utilizing a standardized protocol
Bibliographic record
Abstract
Background: Electroencephalogram (EEG) is used in evaluating thalamocortical function in comatose patients. EEG reactivity is increasingly being recognized as a potentially important predictor of outcome in comatose patients. There are no existing guidelines or standardized testing for EEG reactivity assessment. We will report the use of a clinically implemented standardized reactivity testing protocol in comatose patients to determine accurate prognosis. Methods: In this retrospective study we report results from standardized reactivity testing from January 2016 to May 2016. Five stimuli (Calling name, clapping, nasal tickle, noxious stimulus, tracheal suctioning) were applied at one minute intervals in comatose patients of all etiologies. The EEG background reactivity will be analyzed by two independent electroencephalographers ad correlated to clinical outcome. Results: The methods for establishing EEG reactivity and the inter-rater reliability in determining EEG reactivity will be reported. Conclusions: EEG background reactivity is likely beneficial in determining prognosis. However, reliable methods for eliciting and determining EEG reactivity in comatose patients are necessary.
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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 | 0.008 | 0.022 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".