Association of Qi-stagnation constitution and subjective sleep characteristics with mild cognitive impairment among elderly in community: A cross-sectional study
Bibliographic record
Abstract
Introduction Depression and sleep disturbance are commonly reported in patients with mild cognitive impairment (MCI). However, it remains unclear whether Qi-stagnation constitution is a risk factor for MCI before older adults suffer from depression. Methods Subjects were recruited from 34 community elderly day care centers in China. Intensive face-to-face interviews were conducted using Montreal cognitive function assessment, AD8 dementia screening questionnaire, Pittsburgh Sleep Quality Index (PSQI), and Traditional Chinese medicine constitution assessment scale. Multi-factor logistical regression was employed to analyze the association among subjective sleep quality, TCM constitution, and MCI. Results A total of 1,071 cases were analyzed in this study, including 314 patients with MCI. The probability of those with Qi-deficiency and Qi-stagnation suffering from MCI was 1.559 times and 1.706 times higher than that of the older adults without Qi-deficiency and Qi-stagnation, respectively ( P <0.05). In the PSQI scale, individuals with MCI had poorer subjective sleep quality, longer sleep latency, shorter sleep duration, and aggravated daytime dysfunction ( P <0.05) compared with those without MCI. The results of multi-factor logistical regression showed that sleep latency (OR=1.168), daytime dysfunction (OR=1.261), and Qi-stagnation (OR=1.449) were risk factors for MCI; the OR of suffering from MCI in the elderly with sleep disturbance and Qi-stagnation was 2.581. (all P <0.05). Conclusion MCI patients have a higher prevalence of sleep disorders and Qi-stagnation, and may show specific changes in their daytime and nighttime sleep characteristics, with the specific manifestations such as difficulty in falling asleep, easily waking up at night/ early morning, and daytime dysfunction, among others. Trial Registration Chinese Clinical Trial Registry ChiCTR2000039411
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".