Bibliographic record
Abstract
Prognosis for seizure control and cognitive development varies considerably among syndromes. Several factors may interact to influence outcome of an epilepsy including a causative etiology, ictal and interictal discharges, seizure-related trauma or systemic perturbations, and antiepileptic drug (AED) effects. Clinical evidence convincingly supporting Gowers' hypothesis that seizures beget seizures is lacking. Short-term seizure suppression by early treatment does not appear to influence long-term prognosis. Malignant epilepsy syndromes usually begin in infancy or childhood, have a high seizure frequency, resist the initial AED, and are often associated with progressive cognitive dysfunction. Prompt management of some severe epilepsy syndromes may lessen cognitive decline. However, aggressive AEDs therapy must be balanced against the potential for cognitive side effects, particularly if multiple AEDs are used. Several experimental paradigms closely parallel human TLE as both have an initial precipitating injury (IPI), a latent period, then recurrent spontaneous seizures. In humans, an IPI is any medical event with neurological implications. Although transition from a latent period to a seizure disorder certainly constitutes "progression" of the disorder, convincing clinical evidence of subsequent worsening has not emerged. Substantial clinical and experimental evidence indicates some cognitive regression and focal atrophy with time for TLE and other intractable syndromes. However, seizure frequency and severity, established early in the disorder, appear stable in most patients, and even regress in benign syndromes. Factors mitigating or extinguishing epilepsies need to be further sought.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".