COGNITIVE, PHYSICAL, AND PSYCHOLOGICAL FUNCTION AND QUALITY OF LIFE IN PATIENTS WITH STROKE—A NETWORK ANALYSIS
Bibliographic record
Abstract
Abstract This study aimed to identify the dynamic associations across cognitive, physical and psychological function, and quality of life using network analysis among Chinese patients with acute ischemic stroke (AIS). We conducted a cross-sectional study in 2021 that included a total of 636 patients with AIS from three stroke centers in Shanghai, Nanjing, and Linyi, China. Participants completed a complex battery of measures of cognitive function (Montreal Cognitive Assessment, MoCA), physical function (Barthel Index, Modified Rankin Scale), psychological function (Epidemiological Studies Depression Scale, CES-D) and quality of life (short version of Stroke-specific Quality of life scale, SS-QOL). We used the Gaussian Graphical Modeling to estimate the network structure. Cognitive and physical function were important prerequisites to quality of life. Specifically, the most central nodes (strength) in the network were quality of life, attention (cognitive domain), transferring to a chair, caring perineum/cloth at toilet and walking (physical domain). Cognitive function, particularly visuospatial /executive function and attention, and physical (dressing, feeding and drinking, up and downstairs and transferring to a chair) were positively related to quality of life; whereas depressive symptoms and disability were negatively related to quality of life. Factors contributing to quality of life are complex in patients with AIS. Attention, visuospatial /executive function from cognitive function and transferring, caring at toilet and walking from physical function were central nodes to the quality of life network. Interventions that target cognitive and physical function may have a potential to improve quality of life for stroke patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".