Bibliographic record
Abstract
The review by Reid et al 1 expands upon several issues discussed in a recent commentary. 2 They have focused on information gleaned from animal models.A multicentre collaborative effort, the FEBSTAT study, has addressed issues related to prolonged febrile seizures; 3 the study offers evidence to suggest that non-continuous seizures are underdiagnosed in the emergency room, and their recognition and treatment are important to prevent long-term sequelae.The commentary and review reinforce the need to re-explore febrile seizures incorporating current knowledge.2,3 We must move beyond the classification of 'not complex' (simple) and 'complex' febrile seizures, though the concept remains seminal.4 Central to further progress would be the early precise recognition of seizure semiology and the consideration of syndromes in which febrile seizures constitute only one facet.A prospective Canadian population-based study through the Canadian Pediatric Surveillance Program (CPSP), the Canadian Pediatric Epilepsy Network (CPEN) and the Canadian League against Epilepsy (CLAE) would achieve this purpose and provide for current evidence-based management.Furthermore, children enrolled in the study could be followed up into adult life to clearly establish more current links between febrile seizures in childhood and epilepsy in later life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".