MétaCan
Menu
Back to cohort
Record W2914051241 · doi:10.1161/str.50.suppl_1.wp563

Abstract WP563: Risk Factors Related to the Development of Post Stroke Cognitive Impairment in Shanghai Stroke Population

2019· article· en· W2914051241 on OpenAlexaboutno aff
Yi Dong, Mei Cui, Min Fang, Li Gong, Xiuzhe Wang, Xiaofeng Xu, Zhuojun Xu, Yue Zhang, Lingyun Chen, Xueyuan Liu, Gang Li, Yuwu Zhao, Xiang Han, Qiang Dong

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Logistic regressionOdds ratioInternal medicineRisk factorMontreal Cognitive AssessmentDementiaPopulationCognitionObservational studyPhysical therapyPsychiatryDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.021
GPT teacher head0.267
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueStrokeSame topicNeurological Disease Mechanisms and TreatmentsFrench-language works237,207