P.106 Impact of comorbid sleep disorders in patients with epilepsy on mortality risk
Bibliographic record
Abstract
Background: Pooled mortality has been observed to be almost threefold among people with epilepsy (PWE). Among PWE, epilepsy related deaths (including sudden death in epilepsy (SUDEP)), are commoner than other causes. Among these, around 16-36% are SUDEP, of which 80% events occur during sleep. SUDEP risk is most measured using the SUDEP Risk Inventory. To prevent SUDEP and reduce epilepsy related mortality, we need a better understanding, not only of the components of this screening inventory, but also additional clinical and neurophysiologic parameters that might be commoner among PWE and potentially associated with higher mortality risk. Methods: Patients diagnosed with active epilepsy over the last 3 years will form the study population, categorized into two groups: PWE with a comorbid sleep disorder, and PWE without diagnosis of a sleep disorder. Descriptive statistics will be used to report clinic and neurophysiologic characteristics of subjects enrolled. Results: We hypothesize that there is a significantly increased prevalence of sleep comorbidity among people with epilepsy compared to the general population. Poorer sleep quality could potentially have an association with higher mortality risk among epilepsy patients, Conclusions: We hope to identify and to add sleep factors such as primary sleep disorders and sleep disturbances to already established SUDEP-7 parameters.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".