Targeted Whole Exome Sequencing in Children With Early-Onset Epilepsy: Parent Experiences
Bibliographic record
Abstract
This study investigated the experiences of 25 caregivers of children with early-onset, treatment-resistant epilepsy who pursued whole exome sequencing to determine the impact of the test results on their child's treatment. Caregivers who consented to be recontacted were recruited from a previous study investigating the diagnostic yield of whole exome sequencing. A semistructured interview addressed questions based on one of 2 study phases. The first phase discussed the decision-making process for genetic testing (15 interviews), which revealed 4 major themes: (1) prognosis, (2) engagement, (3) concerns, and (4) autonomy. The second phase discussed the impact of genetic testing on treatment (10 interviews), which revealed 3 major themes: (1) testing features, (2) emotional impact, and (3) treatment outcomes. Overall, parents pursued genetic testing to obtain a clear prognosis, inform treatment decisions, engage with other families, and exercise autonomy. Caregivers felt that early testing is warranted to inform their child's diagnostic odyssey.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".