The relationship between social and psychological factors with cognitive impairment after stroke: a prospective study
Bibliographic record
Abstract
Objectives To investigate the association between social and psychological factors and the risk of cognitive impairment following acute ischemic stroke. Materials and methods A prospective study was conducted at Shanghai Tenth People’s Hospital from June 2021 to July 2022. The study focused on social and psychological factors, which were assessed using the Social Support Rating Scale (SSRS), Self-Perceived Burden Scale (SPBS), and Hamilton Depression Scale (HAMD) within 3 days after admission to the hospital. Cognitive function was evaluated using the Montreal Cognitive Assessment at 3 months post-stroke. Logistic hierarchical regression models were used to examine the association between these three indicators and cognitive impairment following a stroke. Results Among these patients, cognitive function was assessed in 211 cases at the 3-month follow-up after the initial stroke event. At 3 months post-stroke, 118(55.9%) of the participants experienced cognitive impairment, while 93(44.1%) did not. The scores on the SPBS and HAMD showed significant associations with cognitive impairment at 3 months after stroke. The scores of SPBS [scores: 30~39 vs.<20 points, odds ratio (OR)=2.993 (1.135–7.896); scores: ≥40 vs.<20points, OR=7.382 (1.117–48.799); P =0.043] and the HAMD [scores: >7 vs.≤7 points, OR=3.287(1.362~7.936); P =0.008]. There were no significant associations observed between SSRS and PSCI. Conclusion Early screening for depressive symptoms and focusing on self-perceived burden can be beneficial for decision support for clinicians and improve cognitive function recovery at the 3-month mark post-stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".