Why child neurologists talk about SUDEP: Results from two cross‐sectional surveys
Bibliographic record
Abstract
Objective: To characterize SUDEP discussion practices of child neurologists approximately 6 and 12 months after publication of the American Academy of Neurology SUDEP Clinical Practice Guideline and explore factors associated with discussion practice. Methods: Child Neurology Society members (~2450) were electronically surveyed in November 2017 and May 2018 regarding their practice of discussing SUDEP with patients with epilepsy or their caregivers. Multivariable proportional odds ordinal logistic regression evaluated factors associated with discussing SUDEP with a greater proportion of epilepsy patients/caregivers. Reasons for changing practice were described. Results: Among the 369 child neurologist respondents, 36% reported discussing SUDEP with at least half of their epilepsy patients/caregivers including 12% who discuss with all or almost all (>90%) of their epilepsy patients/families. Those who discussed SUDEP with an increased proportion of their patients were more likely to agree that they knew enough to talk about SUDEP, agree that healthcare providers have an ethical obligation to discuss SUDEP, and disagree that there is not enough time to talk about SUDEP. Those who agreed SUDEP could provoke excessive anxiety or worry were less likely to discuss SUDEP with an increased proportion of their patients. Reading the SUDEP Clinical Practice Guideline was a frequently cited reason among respondents who reported a recent change in discussion practice. Significance: Most child neurologists do not follow the current SUDEP Clinical Practice Guideline regarding SUDEP discussion. Feeling sufficiently knowledgeable and ethically obligated to discuss SUDEP were associated with increased discussion practice, suggesting an educational intervention may be effective at increasing SUDEP discussion rates.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".