C.1 The molecular diagnostic landscape of children with seizure onset in the first three years of life
Bibliographic record
Abstract
Background: To clarify the landscape of molecular diagnoses (MDs) in early-onset epilepsy individuals, we determined the prevalent MDs stratified by age at seizure onset (SO) and the time to MD in children with SO <36 months of life. Methods: A panel of up to 302 genes associated with epilepsy was utilized and ordering physicians provided the age of SO. Diagnostic yield analyses were performed for SO ages including <1 mo, 1-2 mo, 3-5 mo, 6-11 mo, 12-23 mo, and 24-35 mo. The time to MD (MD age - SO age) was determined for the top 10 genes in each SO category. Results: 15,074 individuals with SO <36 months of life were tested. Predominant MD findings are as follows: KCNQ2 in neonates with SO at <1mo, KCNQ2 and CDKL5 for SO between 1-2 mo, PRRT2 and SCN1A for SO between 3-11 mo, and SCN1A for SO between 12-36 months. The median time to MD varied by gene. For example, there was no delay in the median time to MD for the GLDC, KCNQ2, and SCN2A genes while the median delay for MECP2, SLC2A1, and other genes was ≥ 12 months. Conclusions: These data highlight the importance of comprehensive early testing in children with early-onset epilepsy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".