Cognitive impairment and risk factor survey in patients with ischemic stroke in Beijing communities
Bibliographic record
Abstract
Objective To investigate the prevalence and risk factors of post-stroke cognitive impairment(PSCI) in patients with ischemic stroke at community health service stations.Methods Five community health service stations were selected from 5 urban areas of Beijing,and a study was performed in patients with first or recurrent attack of ischemic stroke at the community health service stations from January 2003 to December 2004.The mini mental state examination(MMSE) was used to assess the cognitive function of the patients,and the data about the onset of stroke and the risk factors of stroke were collected.Results A total of 993 patients were completed the MMSE evaluation,the incidence of PSCI was 7.9%(78/993).The prevalence of PSCI within 6 months,7-12 months,and 12 months after onset of stroke were 8.5%,10.1%,and 4.9%,respectively.The prevalence of PSCI in patients under 60 years of age was 3.9%,and the prevalence in 60-65,65-70,and ≥70 year groups were 5.1%,8.1%,and 11.4% respectively(χ2 trend test =12.521,P0.0001).The prevalence of PSCI was varied with the educational level of the patients,in patients with college graduates was 3.6%,in patients with middle,primary school education and illiterate were 1.72,3.94,and 4.04 times of the college graduates(χ2 trend test=13.694,P0.0001).The impact factors of PSCI were the multi-focal stroke,depressive state,activities of daily living handicap,and non-lacunar infarction of which,the first three were the independent risk factors for PSCI.Conclusion PSCI in patients with ischemic stroke is associated with a variety of factors,such as the course of disease,educational level,age of disease onset etc.The multi-focus stroke,depressive state,and activities of daily living handicap are the independent risk factors for PSCI.
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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 | 0.002 |
| 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".