[The evolution of cognition and its influence factors after stroke].
Bibliographic record
Abstract
OBJECTIVE: To investigate the evolution of cognitive function and its influence factors, so as to provide evidence for guiding treatment of cognitive impairment after stroke. METHODS: A total of 98 cases of patients with stroke admitted in the First and Second Affiliated Hospital of Medical College of Xi'an Jiaotong University and Shaanxi Provincial People's Hospital between April and September 2009 were enrolled and recruited. Mini-mental state examination (MMSE) and Montreal cognitive function rating scale (MoCA) were adopted to assess the evolution of cognition at acute phase (within 2 weeks), 6 weeks, and 12 weeks after stroke among patients within 2 weeks after onset, questionnaire score ≤ 56, without aphasia and consciousness disturbance and at least one side of upper extremities muscle force ≥ grade 3. RESULTS: When using MMSE scale as criteria, the incidence of cognitive impairment was 24.5% at acute phase, 12.1% at 6 weeks and 9.9% at 12 weeks after stroke, while the incidence was 86.8%, 68.2%, and 38.0% respectively when using MoCA scale as criteria. The scales of MMSE and MoCA were increased and the incidence of cognitive impairment was decreased within 12 weeks after stroke. Logistic regression analysis indicated that, advanced age (β = -0.124), hypertension (β = -3.705), low education level(β = 0.560) and depression after stroke (β = 4.613) were related with cognitive impairment after stroke (all P values < 0.05); low education level(β = 0.710), coronary heart disease (β = -3.649), elevated total cholesterol (TC) (β = -3.361) and low density lipid cholesterol (LDL-C) (β = -5.833), and depression (β = -3.612) delayed recovery of cognition after stroke. CONCLUSIONS: The cognitive function improves and the incidence of cognitive impairment lowers as the time goes on within 12 weeks after stroke. The factors that may affect the improvement of cognitive function include low educational level, coronary heart disease, elevated TC and LDL-C, and post-stroke depression.
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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 | 0.000 | 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) | 0.001 | 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".