Sleep symptoms in syndromes of frontotemporal dementia and Alzheimer’s disease: A proof-of-principle behavioural study
Bibliographic record
Abstract
Sleep disruption is a key clinical issue in the dementias but the sleep phenotypes of these diseases remain poorly characterised. Here we addressed this issue in a proof-of-principle study of 67 patients representing major syndromes of frontotemporal dementia (FTD) and Alzheimer's disease (AD), in relation to 25 healthy older individuals. We collected reports on clinically-relevant sleep characteristics - time spent overnight in bed, sleep quality, excessive daytime somnolence and disruptive sleep events. Difficulty falling or staying asleep at night and excessive daytime somnolence were significantly more frequently reported for patients with both FTD and AD than healthy controls. On average, patients with FTD and AD retired earlier and patients with AD spent significantly longer in bed overnight than did healthy controls. Excessive daytime somnolence was significantly more frequent in the FTD group than the AD group; AD syndromic subgroups showed similar sleep symptom profiles while FTD subgroups showed more variable profiles. Sleep disturbance is a significant clinical issue in major FTD and AD variant syndromes and may be even more salient in FTD than AD. These preliminary findings warrant further systematic investigation with electrophysiological and neuroanatomical correlation in major proteinopathies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".