Precision Medicine Approaches for Infantile-Onset Developmental and Epileptic Encephalopathies
Bibliographic record
Abstract
Epilepsy is an etiologically heterogeneous condition; however, genetic factors are thought to play a role in most patients. For those with infantile-onset developmental and epileptic encephalopathy (DEE), a genetic diagnosis is now obtained in more than 50% of patients. There is considerable motivation to utilize these molecular diagnostic data to help guide treatment, as children with DEEs often have drug-resistant seizures as well as developmental impairment related to cerebral epileptiform activity. Precision medicine approaches have the potential to dramatically improve the quality of life for these children and their families. At present, treatment can be targeted for patients with diagnoses in many genetic causes of infantile-onset DEE, including genes encoding sodium or potassium channel subunits, tuberous sclerosis, and congenital metabolic diseases. Precision medicine may refer to more intelligent choices of conventional antiseizure medications, repurposed agents previously used for other indications, novel compounds, enzyme replacement, or gene therapy approaches.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".