P.033 Electrophysiological signatures of sedation in pediatric patients
Bibliographic record
Abstract
Background: Sedation in PICU masks physical exam findings, leading to diagnostic challenges. In adult models, electroencephalography can evaluate the brain’s response to sedation using feedforward connectivity and anteriorization of alpha hubs, proving useful for prognostication. Feasibility of model translation into pediatric population was assessed, with the hypothesis that the same markers of adaptive reconfiguration would correlate with a higher potential for recovering consciousness. Methods: Electroencephalograms from children undergoing sedation were analyzed for strength and direction of functional connectivity using the weighted and directed phase lag index. Target population was refined with an iterative inclusion criteria. We examined relationships between hub location reconfiguration, directed phase lag index, baseline Glasgow Coma Scale, and 3-month post-treatment Glasgow Outcome Scale-Extended. Results: Evaluation of 14 subjects showed promise in children aged 5-18 undergoing sedation with midazolam, dexmedetomidine, and propofol. Further analysis of five subjects revealed a correlation between adaptive reconfiguration during anesthesia and both higher baseline Glasgow Coma Scale and Glasgow Outcome Scale-Extended scores post-treatment. Conclusions: The findings indicate that the functional brain network connectivity model may have diagnostic and prognostic potential regarding children’s consciousness levels. While the initial data is promising, further analysis of six additional cases is pending and deemed essential to thoroughly evaluate the model’s efficacy.
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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 | 0.001 |
| 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) | 0.002 | 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".