A Risk Factor Analysis of Cognitive Impairment in Elderly Patients with Chronic Diseases in a Chinese Population
Bibliographic record
Abstract
BACKGROUND This study analyzed the risk factors of cognitive impairment (CI) in elderly patients with chronic diseases. MATERIAL AND METHODS In total of 385 elderly patients with chronic diseases were selected and assigned into CI and normal groups. The activities of daily living (ADL), global deterioration scale (GDS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment Scale (MoCA), patient-generated subjective global assessment (PG-SGA), and mini nutritional assessment (MNA) were performed to analyze the differences between the 2 groups. Logistic regression analysis was conducted for risk factors of CI in elderly patients with chronic diseases. RESULTS There were differences in age, education level, type 2 diabetes mellitus, multifocal cerebral infarction, hearing, and eyesight between CI and normal groups. Patients in the CI group showed more CD4+ cells, more admission times, and higher GDS scores than the normal group. Also, MMSE and MoCA scores revealed differences in total score, directive force, attention and calculating ability, language, delayed memory, reading comprehension, writing, and visual-spatial ability between the 2 groups. The number of B and CD8+ cells, ADL, and MNA scores were protective factors, while cerebral infarction history, number of CD4+ cells, admission times, GDS score, and age were risk factors of CI in elderly patients with chronic diseases. CONCLUSIONS Our study provides evidence that cerebral infarction history, number of CD4+ cells, admission times, GDS score, and age are risk factors of CI in elderly patients with chronic diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".