Status quo and influencing factors of quality of life in neurological patients with cognitive disorder
Bibliographic record
Abstract
Objective To explore the quality of life in patients with cognitive disorder and to discuss its influencing factors so as to provide a reference for improving the quality of life in patients with cognitive disorder. Methods Totally 1 398 neurological patients from a neuropsychiatric hospital in Nanjing were screened using cluster sampling and 125 patients with cognitive disorder were selected as subjects between January and December 2017. The subjects received a cross-sectional study with Quality of Life in Alzheimer's Disease (QOL-AD) , Montreal Cognitive Assessment Scale (MoCA) , Activities of Daily Living Scale (ADL) , Self-Rating Anxiety Scale (SAS) , Self-Rating Depression Scale (SDS) and the general information questionnaire. Variance analysis, Pearson correlation analysis and multivariate linear regression analysis were used for statistical analysis. Results The morbidity of cognitive disorder in neurological patients from our hospital was 9.3%. Their QOL-AD score was (31.88±6.54) . Sex, educational background, nature of work, household income, sleep and bad social relations are factors affecting the quality of life of the subjects (P<0.05) . According to multivariate linear regression analysis, MoCA, SAS and SDS were main factors affecting the quality of life of the subjects (P<0.05) . Conclusions Male patients with lower education, physical work and bad social relations are high-risk patients with lower quality of life. Cognitive disorder, anxiety and depression are main risk factors affecting the quality of life in patients with cognitive disorder. Nursing workers should improve and maintain the patients' cognitive ability, enrich their cultural life and social activities, and enhance their mental health, thereby improving their quality of life. Key words: Quality of life; Cognitive disorder; Neurology; Influencing factors
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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, 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".