Correlation analysis between sleep quality and mild cognitive impairment in elderly patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Objective To investigate the relationship between sleep quality and mild cognitive impairment(MCI) in elderly patients with type 2 diabetes mellitus(T2DM).Methods We collected 97 elderly patients with type 2 diabetes mellitus and divided them into two groups: MCI group(DM-MIC) and normal cognition group(DM-NC group).All subjects were evaluated with cognition scores of Mini-mental state examination(MMSE),Montreal Cognitive Assessment(MOCA),Self-Rating Anxiety Scale(SAS),Center for Epidemiogical Studies Depression Scale(CES-D),Activity of daily living(ADL),Global Deterioration Scale(GDS),and Pittsburgh sleep quality index(PSQI).Independent t-test and Spearman's rank correlation analysis were performed.Results The hypnotic use in DM-MCI group was significantly more than that in DM-NC group(P=0.046),and it had a negative correlation with the score of delayed recall,but not statistically significant(r=-0.277,P=0.062).Significantly negative correlations were also found between the score of PSQI and MOCA(r=-0.362),PSQI and attention(r=-0.319),PSQI and delayed recall(r=0.409),daytime dysfunction and MOCA(r=-0.393),daytime dysfunction and abstraction(r=-0.337),night sleep disorders and language(r=-0.439),sleep efficiency and delayed recall(r=-0.325),and subjective sleep quality and delayed recall(r=-0.336) in DM-MCI group(P0.05).Conclusion Sleep quality is correlated with some symptoms of mild cognitive impairment in elderly patients with T2DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".