Cognitive impairment after first-ever ischemic stroke
Bibliographic record
Abstract
Background and aim Ischemic stroke has a good outcome because these patients usually have a good motor recovery. The aim of this work was to assess the prognostic value of the neurocognitive status to detect early cognitive dysfunction in stroke phases, evaluate outcome after first-ever ischemic stroke, and to choose proper preventive management of stroke cognitive dysfunction. Patients and methods Patients with ischemic stroke were prospectively evaluated using Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) individually and in combination with National Institutes of Health Stroke Scale (NIHSS), either at the subacute stroke phase or within 2 weeks (baseline), and modified Rankin scale (mRS) scores, for functional outcome 3 and 6 months later. Results Cognitive impairment was diagnosed at baseline in 37.5% of patients with median NIHSS=4 and median mRS=2 ( P <0.001). Baseline NIHSS, MMSE, and MoCA can individually predict mRS scores at 3 and 6 months, and NIHSS is the strongest predictor. However, patients with more disability at baseline (NIHSS>2), baseline MoCA, and MMSE had a moderately large significant predictive value to the baseline NIHSS for mRS scores at 3 and 6 months. Conclusion Screening of cognitive state at the subacute stroke phase can predict functional outcome independently and improve the predictive value of stroke severity scores. And it is important to evaluate what cognition is, and the brief cognitive test may facilitate assessment in the early phases.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".