Semantic clustering and sleep in patients with amnestic mild cognitive impairment or with vascular cognitive impairment-no dementia
Bibliographic record
Abstract
BACKGROUND: Cognition and sleep deficits occur in amnestic mild cognitive impairment (aMCI) and vascular cognitive impairment-no dementia (VCIND). However, how memory and sleep deficits differ between aMCI and VCIND remains unclear. METHODS: Fifty aMCI and 50 VCIND patients and 38 sex- and age-matched healthy controls (HCs) were administered the Hopkins Verbal Learning Test-Revised (HVLT-R), Trail Making Test-A/B (TMT-A/B), Wisconsin Card Sorting Test (WCST), Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Benton Judgment of Line Orientation (JLO) test, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Insomnia Severity Index (ISI) to quantify cognitive deficits and subjective sleep disturbance. RESULTS: Compared with VCIND patients, aMCI patients had lower HVLT-R scores for total recall (p < 0.001), delayed recall (p < 0.001) and recognition (p = 0.001), and for total-recall (p = 0.002) and delayed-recall (p < 0.001) semantic clustering ratios (SCRs). However, VCIND patients exhibited more obvious executive dysfunction (TMT-A, p < 0.001; TMT-B, p < 0.001; WCST, p < 0.001), lower information processing speed (PASAT, p = 0.003; SDMT, p < 0.001), and more severe sleep disturbance (PSQI, p < 0.001; ESS, p < 0.001; ISI, p < 0.001). Additionally, sleep quality and efficiency were related to total and delayed recall (all r values from -0.31 to -0.60, p < 0.05) in aMCI and VCIND. CONCLUSIONS: aMCI and VCIND differ in cognitive function, memory strategy and sleep impairment; these characteristics are helpful to identify and distinguish patients with very early cognitive impairment. Our results also suggest that memory deficits are associated with sleep disturbance in aMCI and VCIND.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".