Detecting Seizures from a Low-density Montage with BrainsView
Bibliographic record
Abstract
Critically ill paediatric patients are at increased risk of having seizures without apparent clinical signs making clinical diagnosis particularly difficult. Undetected or delayed treatment of seizures worsens these patients’ functional neurological recovery. <br/>An electroencephalogram (EEG) is the gold standard method to detect seizures. Certified clinical physiologists are required to apply high density montages and neurologists are needed to interpret the recordings and identify seizures. Neither are available round the clock in the paediatric critical care units (PCCU). Thus, there is a clinical need to develop a quantitative seizure detection method using a low-density EEG montage, which may be applied by the bedside nurses in PCCU. In this project, we aim to test and adapt the BrainView’s brain connectivity assessment software to detect seizures using only 8 channels from routinely collected multi-channels EEG. <br/>
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".