Communication about sudden unexpected death in epilepsy: Understanding the caregiver perspective
Bibliographic record
Abstract
Objective: We aimed to characterize (1) the caregiver experience of learning about sudden unexpected death in epilepsy (SUDEP), and (2) caregiver preferences for SUDEP risk disclosure. Methods: We distributed a 24-question survey to caregivers of children with epilepsy. Free text questions were analyzed using a rapid qualitative analysis approach. Results: Two hundred and twelve caregivers of people with epilepsy completed the survey, including 12 bereaved caregivers. Caregivers' children had a high seizure burden, with a median seizure frequency of 24 seizures per year (range: 1 to ≥100). Most participants were aware of SUDEP at the time of the survey (193/212; 91%) though only a minority had learned about SUDEP from a healthcare provider (91/193; 47.2%). Caregivers typically learned about SUDEP from a nonprofit or online source (91/161; 56.5%). Almost all caregivers wanted to discuss SUDEP with their child's healthcare provider (209/212; 98.6%), and preferred disclosure from epileptologists (193/212; 91%), neurologists (191/212; 90.1%), and/or primary care providers (98/212; 46.2%). In open-ended responses, caregivers highlighted the value of learning about SUDEP from a healthcare provider, the importance of pairing SUDEP risk disclosure with a discussion of how to mitigate risk, and the need for educational resources and peer support. Interpretation: Caregivers of people with epilepsy appreciate when healthcare providers disclose information about SUDEP, yet typically hear about SUDEP elsewhere. These findings underscore the importance of interventions to improve and support SUDEP risk disclosure. Future work should evaluate strategies to disclose SUDEP risk and the impact of early SUDEP risk disclosure.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".