Susceptibility to Febrile Seizures: More Than Just a Faulty Thermostat!
Bibliographic record
Abstract
Febrile seizures, always a hot topic, continue to fire up the interest of a wide spectrum of clinical and basic neuroscientists. Several clinical investigators, amongst them the Halifax group (spearheaded by the Camfields to whom we owe a great debt of gratitude for their contributions in this field), have provided us with a sound foundation for clinical management. We now need to explore febrile seizures in new ways to clarify factors and identify mechanisms that contribute to the intriguing age-dependent susceptibility. The complex processes involved in thermoregulation and the febrile response are important pieces of the puzzle. The contributory factors are likely different for isolated simple febrile, recurrent febrile and complex febrile seizures. A 'systems biology approach' is needed to investigate the intricate genome-proteome-metabolome interaction in determining susceptibility. Population studies that incorporate current clinical, experimental, infectious and molecular genetic knowledge in their concept and design will help to 'conquer' the final frontiers of febrile seizures. In 2006, Engel suggested that febrile seizures could 'encompass many different entities', an increasingly plausible opinion. A higher profile for febrile seizures and related syndromes in the ILAE classification scheme will further catalyze progress in the field. The resultant knowledge can only improve management.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".