P.019 A machine learning approach to asymmetric burst suppression and refractory status epilepticus outcome
Bibliographic record
Abstract
Background: Treatment of refractory status epilepticus (RSE) is often titrated to achieve EEG burst suppression. However, optimal burst suppression characteristics are largely unknown. We used an unsupervised machine learning algorithm to predict RSE outcome based on the quantitative burst suppression ratio (QBSR). Methods: We conducted principal component analysis (PCA) as a linear combination of 22 QBSR features from non-anoxic adult RSE patients at the Winnipeg Health Sciences Centre. We also determined the most predictive components that significantly differed between survivors and non-survivors. Results: Using 135,765 QBSRs from 7 survivors and 10 non-survivors, PCA identified a predominantly non-survivor cluster of 8 patients (75% non-survivors). The first 2 PCA components comprised 75% data variance. The most important first component feature was skewness of QBSR distribution in the right or left hemisphere (0.52 each). The most important second component feature was third QBSR quantile of the left hemisphere (0.49). Only right hemispheric QBSR features significantly differed between groups: QBSR skewness for the first component (Benjamini-Hochberg adjusted p=0.038) and average QBSR for the second component (0.32, Benjamini-Hochberg adjusted p=0.046). Conclusions: Our pilot study shows that RSE patient survival may be impacted by QBSR, with differential hemispheric EEG burst suppression characteristics predicting poor RSE outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".