Spectrograms for Seizure Detection in Critically Ill Children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY: Electrographic seizures are common in critically ill children and a significant proportion of these seizures are nonconvulsive. There is an association between electrographic seizures and neurophysiological disturbances, worse short- and long-term neurologic outcomes, and mortality in critically ill patients. In this context, timely diagnosis and treatment of electrographic seizures in critically ill children becomes important. However, most institutions lack the resources to support round-the-clock or frequent review of continuous EEG recordings causing significant delays in seizure diagnosis. Given the current gaps in review of continuous EEG across institutions globally, use of visually simplified, time-compressed quantitative EEG trends such as spectrograms has the potential to enhance timeliness of seizure diagnosis and treatment in critically ill children.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it