Bibliographic record
Abstract
Some epilepsy is, in general, often associated with cognitive problems that can also affect a patient’s adjustments. Epileptic seizures result from an excessive, synchronous discharge of cerebral neurons. Interictal paroxysmal electroencephalogram (EEG) abnormalities are regarded as a correlate of persistent pathological neuronal discharges. Thus, correlation between cognitive deterioration and seizure severities/EEG paroxysmal abnormalities should be investigated. We have previously measured frontal/prefrontal lobe volumes using three-dimensional (3D)-magnetic resonance imaging (MRI) in children with benign childhood epilepsy with centrotemporal spikes, epilepsy with continuous spike-waves during slow sleep, frontal lobe epilepsy, and Panayiotopoulos syndrome, and confirmed that longer active seizure period as frequent spike-waves coupled with the occurrence of frequent seizures and presence of status epilepticus may be associated with prefrontal lobe growth disturbance, which relates to cognitive impairments. These findings suggest that seizure severities such as repeated seizures and presence of status epilepticus, and the subclinical paroxysmal EEG abnormalities may induce prefrontal lobe growth disturbance, which leads to intellectual impairments. Achieving better seizure control and remission for paroxysmal EEG abnormalities is a key to improve quality of life (QOL) in children with epilepsy. From the perspective of decreased cognitive problems and improving QOL, management may be required to remit seizures and paroxysmal EEG abnormalities as soon as possible to achieve optimal prognosis in epilepsy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".