Abstract WP563: Risk Factors Related to the Development of Post Stroke Cognitive Impairment in Shanghai Stroke Population
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) or post-stroke dementia (PSD) may affect one third of stroke patients. Understanding the risk factors related to the development of PSCI or PSD in acute stroke may help prevent and treat PSCI or PSD. A large-scale examination of PSCI risk factors was done for the first time in Shanghai stroke population Method: This was a multicenter observational registry study. The inclusion criteria were: >18-year-old, had an ischemic stroke within 2 weeks. Those with preexisting cognitive impairment, language, impaired hearing or reading or without education were excluded. Once informed consents signed, MOCA, mini-MOCA and MMSE were completed. Rate of PSCI and its related risk factors were examined. Statistical analysis was conducted by STATA 15. Logistic regression was performed to evaluate the risk factors and their odds ratios. Results: Of 1006 AIS patients with stroke onset within 14 days, 516 (51.8%, 95% CI 46.3-58.2%) had PSCI. These patients performed poorly in MOCA spatial, naming, registration, abstraction, language, orientation and short-term memory tests. Multiple regression model showed that patients with advance aging (OR 1.846 95%CI 1.806-2.826), severe stroke (OR 2.182, 95%CI 1.296-3.273), previous stroke history (OR 1.507, 95%CI 1.053-2.057) were likely to develop PSCI in acute phase of ischemic stroke. However, high level of education (OR 0.374, 95%CI 0.273-0.589) and LDL level>1.8 mmol/L may relate to the less PSCI rate in post stroke patients (OR 0.499, 95%CI 0.279-0.892). There is no significant correlation between stroke type and PSCI. Conclusion: In this post-stroke cognitive study cohort, PSCI was common among stroke patients in Shanghai. The risk factors of PSCI are age, previous stroke and less educated, however, the strategy of how to prevent PSCI is still unknown. The paradoxical phenomenon of protective effect of hyperlipidemia in PSCI remains to be further studied.
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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 | 0.000 | 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) | 0.003 | 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".