Sleep symptomatology in the National prion monitoring cohort
Bibliographic record
Abstract
Sleep disturbance in patients with prion disease is a recognized symptom but the frequency, subtypes of prion disease most commonly affected and the pathogenesis remain unknown. This study identifies the prevalence of sleep disturbance, symptomatology and it’s association with disease subtype, brain MRI changes and codon 129 genotype. Analysis of data collected on clinical symptoms, including sleep disturbance, was conducted in 448 patients with prion disease, recruited to the National Prion Monitoring Cohort. MR brain scans from these patients were examined for the presence of thalamic signal change. Logistic regression analysis was used to determine predictors of sleep disturbance. Sleep disturbance was found to be present in 76% of patients recruited to the NPMC and was present in all subtypes of human prion disease. The most commonly reported symptoms were hypersomnolence (62%), waking at night (53%) and insomnia (43%). Sleep dis- turbance was strongly associated with the presence of depression and there was a significant association found between sleep symptoms and abnormal thalamic signal change identified on MR brain imaging. This study highlights the prevalence of sleep disturbance in patients with prion disease, identifies co-morbid symptoms of depression and finds a significant association between abnormal thalamic signal change and sleep disturbance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".