Dreaming in patients with epilepsy: a cross‐sectional cohort study
Bibliographic record
Abstract
Epilepsy impacts cognition during wakefulness. As epileptic activity is present and even augmented during sleep, epilepsy could also influence sleep-related cognitive processes. However, whether epilepsy modulates sleep-related experiences like dreaming remains poorly known. Here, we prospectively investigated the characteristics and determinants of dreaming in patients with epilepsy. Consecutive adult patients with epilepsy and no major cognitive deficit were recruited in an epilepsy outpatient clinic. They completed a questionnaire about their dreams, sleep and epilepsy over the past year. Medical data on epilepsy characteristics were gathered from the medical file. A generalised linear model was used to explore the determinants of dream recall frequency (DRF). We included 300 patients, with a mean (standard deviation [SD]) age of 40.4 (13.4) years and 51.3% female; 28.6% had more than one seizure/month, and 34.7% already had seizures during sleep. Patients recalled dreams on an average of 1.6 (1.5) days/week and 11% had one or more nightmare/week. Younger age, higher number of nocturnal awakenings, and lower seizures frequency predicted a higher DRF. In patients with focal epilepsy (65.3%), the localisation of the epileptic focus in the parieto-occipital area was negatively associated with DRF. Regarding dream content, 34.0% of patients reported having already dreamt about epilepsy. Dreams of seizures were associated with sleep-related seizures (p = 0.034) and dreams of epilepsy were associated with nightmare frequency (p = 0.004). Our results show that patients with epilepsy share several determinants of DRF (age, awakenings, role of the parieto-occipital area) with healthy subjects. In addition, epilepsy-related factors (seizure frequency, focus localisation) also impact DRF. Investigating dreams in patients with epilepsy can provide information on their epilepsy and their sleep.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".