Incidence and Factors Associated With Cognitive Impairment 90 Days After First Ever Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVES: Post-stroke cognitive impairment (PSCI) is prevalent among stroke survivors, negatively impacting long-term outcomes. We aimed to assess the prevalence of PSCI and its risk factors in participants from the iBioStroke study (n = 1042), 90 days after their first ischemic stroke. METHODS: We prospectively analyzed data from 582 participants, without cognitive problems before stroke based on the structured interview with the patient, a family member or a caregiver, and/or clinical documentation (if available), who completed the Montreal Cognitive Assessment (MoCA) at discharge and 90 days post-stroke. Two MoCA cut-offs were used to define PSCI: a score of ≤ 25 in the first model and ≤ 22 in the second model. Multivariate logistic regression was employed to identify independent risk factors for PSCI based on 30 collected parameters. RESULTS: In the first model, PSCI was identified in 418 (71.8%) participants at day 90. Independent risk factors included older age (OR = 1.05; 95% CI:1.02-1.08), fewer years of education (OR = 0.83; 95% CI: 0.73-0.93), lower MoCA scores at discharge (OR = 0.76; 95% CI: 0.69-0.84), higher anxiety levels (HADS-A) at day 90 (OR = 1.10; 95% CI: 1.01-1.21), and larger stroke volume (OR = 1.01; 95% CI: 1.00-1.01). In the second model, PSCI was observed in 294 (50.5%) participants. Older age (OR = 1.06; 95% CI: 1.03-1.09), fewer years of education (OR = 0.87; 95% CI: 0.78-0.96), lower MoCA scores at discharge (OR = 0.83; 95% CI: 0.77-0.88), and higher depression levels (HADS-D) at day 90 (OR = 1.10; 95% CI: 1.03-1.18) were significant predictors. CONCLUSIONS: Based on our data, PSCI seems to be a common consequence of stroke. Both irreversible factors, such as age and educational level, stroke volume, and potentially modifiable factors, including post-stroke anxiety or depression and acute cognitive impairment, contribute to PSCI risk. These findings underscore the importance of early cognitive and psychiatric interventions in stroke survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".