An evidence‐based approach to the first seizure
Bibliographic record
Abstract
Evidence-based care (EBC) is an explicit approach to applying the best evidence to the care of individual patients. We outline the basic principles of EBC and apply them to various clinical questions pertaining to a patient presenting with a first seizure, providing a summary of the best available evidence for each question. Depending on the question at hand, the evidence derives from retrospective, prospective, and randomized controlled studies in children and adults. There is solid evidence that early seizure recurrence is reduced by early initiation of AEDs. A meta-analysis of six randomized trials revealed an average absolute risk reduction of 34% (95% CI 15-52) with AED therapy. However, the prognosis for the development of epilepsy is not altered by early intervention. EEG epileptiform abnormalities, family history of epilepsy, imaging lesions, and remote symptomatic seizures increase the risk of recurrence, and impact the risk-benefit ratio of treatment after a single event. In the end, clinicians must evaluate patients with a first unprovoked seizure on a case-by-case basis to determine the appropriateness of treatment with a given AED.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".