Importance of Retesting for the Final Diagnosis of Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
Background and Objectives: Post-stroke cognitive impairment (PSCI) has been defined as all problems in cognitive function that occur following a stroke. Studies published thus far on the prevalence of PSCI and post-stroke dementia (PSD) have shown conflicting estimates. The aim of this study was screening for cognitive impairment (CogI) in patients with an ischaemic stroke and finding the relationship between CogI (and its changes) and cardiovascular risk factors and imaging procedures—CT/MRI. Materials and Methods: We prospectively included patients with an ischaemic stroke admitted in the period from October 2019 to May 2022. In this period, 1328 patients were admitted, 305 of whom met the established inclusion criteria and underwent an examination of cognitive functions using the Montreal Cognitive Assessment (MoCA). Of these, 50 patients appeared for the control examination after 6 months. Results: In the retested group, CogI at discharge was diagnosed in 37 patients (74%). In follow-up testing after 6 months, CogI was present in 30 patients (60%). Only arterial hypertension (OR: 15; 95% CI; Pearson r: 0.001), lower education level (less than 13 years) (OR: 9.7; 95% CI 2.0–48.5; Pearson r: 0.002), and higher age were significantly associated with CogI after stroke. Conclusions: We established the prevalence of CogI and its course after 6 months in a well-defined group of patients after a mild ischaemic stroke (mean NIHSS: 2 and mean mRS: 1 at the discharge). Our results show that the prevalence of CogI after an ischaemic stroke at discharge is relatively high (74%), and it tends to be a spontaneous reduction. Cognitive functions were changed in 35% of patients. The definition of PSCI was completed in only 24% of individuals. Only an examination several months after a stroke can give us more accurate information about the true prevalence of persistent CogI after a stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".