Bibliographic record
Abstract
We aimed to investigate subjective sleep quality and polysomnographic sleep structure features in patients with vascular cognitive impairment-no dementia (VCIND).Fifty-six patients with VCIND, 48 patients with simple stroke (without cognitive impairment), and 48 control subjects were included. The Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were used to analyze their sleep characteristics. The Montreal Cognitive Assessment (MoCA) was conducted to assess mental state.Patients with VCIND had higher PSQI scores compared with control subjects and simple stroke patients (P < .01). PSG revealed that patients with VCIND or stroke were more likely to experience prolonged sleep latency (SL), decreased sleep efficiency (SE), increased arousal, and reduced deep sleep and rapid eye movement (REM) sleep than controls. Patients with VCIND had significantly longer SL, increased periodic leg movements in sleep (PLMS), decreased SE, and increased arousal and sleep fragmentation compared to patients with simple stroke (P < .05). In VCIND patients, a significant positive correlation was found between SE and MoCA scores (r = 0.632; P < .001), though PSQI, SL, and arousal index were significantly negatively associated with MoCA scores (r = −0.787, −0.740, −0.772, respectively; P < .001 for all).VCIND patients had different abnormal sleep features, including decreased SE, increased PLMS, and prolonged SL and sleep fragmentation. Abnormal sleep in VCIND may be associated with cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.609 | 0.376 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".