Continuous EEG Monitoring in Severe Guillain-Barré Syndrome Patients
Bibliographic record
Abstract
When patients Guillain-Barré syndrome have complete paralysis clinical measures of sedation cannot be applied. In this situation continuous EEG offers a convenient, effective method of monitoring the depth of sedation, using spectral edge frequency (SEF) to quantify EEG activity. The authors report 3 patients with severe Guillain-Barré syndrome managed with sedation aimed at a SEF95 below 4.0 Hz (delta coma), using a subhairline montage with the DATEX bedside EEG module. Two of the patients were easily managed using this system for an average of 16 days, and both were completely amnestic of this period of time with no serious complication. The third one had still some residual muscle activity and SEF was unreliable in this case, so its use was abandoned. Continuous EEG monitoring using SEF is a useful tool to manage sedation in the most severely paralyzed Guillain-Barré syndrome patients. Incorporation of a low-pass filter would be of benefit to remove any residual muscle activity, which confounds the target level of sedation with this method; SEF has theoretical advantages over the bispectral index in this population. Comparative studies of various continuous EEG monitoring methods in such patients should better define their relative effectiveness.
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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 | 0.000 | 0.002 |
| 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) | 0.000 | 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 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".