Sleep structure of short-term insomnia disorder with mild cognitive impairment in older adults and their correlation with cognitive function: a case-control study
Bibliographic record
Abstract
Objectives This study seeks to delineate the sleep architecture characteristics in older adults with short-term insomnia and mild cognitive impairment (MCI) and to explore their association with cognitive performance. Methods Ninety elderly individuals with short-term insomnia were enrolled and stratified into two cohorts based on their Montreal Cognitive Assessment (MoCA) scores: the Short-Term Insomnia Group (STID) comprising 35 participants and the Short-Term Insomnia with Cognitive Impairment Group (STID-MCI) with 55 participants. Demographic data, Pittsburgh Sleep Quality Index (PSQI), MoCA, Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), and polysomnography (PSG) parameters were compared between groups. Correlations between MoCA scores and PSG metrics were also analyzed. Results No significant disparities were noted between groups in terms of HAMD-17, HAMA, and PSQI scores (p > 0.05). However, marked differences were identified in MoCA scores and its subdomains (p < 0.05). Significant variations were also observed in the duration and proportion of slow-wave sleep (N3) between groups (p < 0.05). In STID-MCI patients, memory scores correlated positively with N3 duration and percentage (p < 0.05), while verbal functions and attention were positively associated with rapid eye movement (REM) sleep duration. Conclusion This study highlights the potential of PSG in the clinical assessment of cognitive function and underscores the need for targeted interventions to improve sleep quality in this vulnerable population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".