A meta analysis of key risk factors for sudden unexpected death in epilepsy
Bibliographic record
Abstract
Purpose: To examine the risk factors (RFs), associated with Sudden Unexpected Death in Epilepsy (SUDEP), and the quantitative standards required to measure them Methods: The literature on RFs associated with SUDEP was systematically reviewed up to August 2020 in databases, including PubMed, the Cochrane Database and Embase. Revised Newcastle-Ottawa Scale (NOS) was performed to determine the quality of each study in this meta-analysis (MA), with a score of ≥ 3, indicating good quality. Any controversies in data extraction and quality assessment were resolved through counsel or adjudication with a third researcher. Results: An initial screening of the literature following the search strategy and manual inclusion yielded a total of 767 studies. After excluding duplicates as well as articles that did not match the topic, 112 studies remained. Twenty-nine studies were finally selected based on the inclusion and exclusion criteria. After a careful review of the full text, nine studies were included in the MA. Conclusion: The five RFs for SUDEP included age at the onset of epilepsy ≤15 years, generalized-tonic-clonic seizure, seizure frequency ≥50 seizures/year, treatment with a combination of multiple antiepileptic drugs, and history of alcohol abuse.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.056 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".