Pridection of cognitive dysfunction in the early outcome after acute stroke
Bibliographic record
Abstract
Background Stroke is a critical disorder that affects ~13.7 million people worldwide each year. Patients who suffer from a stroke frequently develop abnormalities in cognitive abilities. Aim We aimed to study the factors and parameters through which we can predict the occurrence of cognitive impairment in patients suffering an acute stroke. Patients and methods A prospective cohort study included 60 acute cerebral stroke patients who attended Neuropsychiatry Department of Tanta University Hospital. All the patients were examined by using the Montreal Cognitive Assessment, mini-mental state examination (MMSE), verbal fluency, and clock drawing test twice, once in the early few days after the stroke and the other at three months after stroke onset for evaluation of the cognition. Results In the first visit, poststroke cognitive assessment (in the early few days after stroke) using Montreal Cognitive Assessment, MMSE, verbal fluency and clock drawing tests, hypertension, high severity of the stroke, increasing age, dyslipidemia, ischemic type of stroke, male gender were the significant risk factors for the early cognitive dysfunction while in the second visit, dyslipidemia, hypertension, increasing severity of the stroke and increasing age were the significant risk factors for the late cognitive dysfunction by 3 months after stroke onset. Conclusion Poststroke cognitive function was assessed using Montreal Cognitive Assessment, MMSE, and Verbal Fluency Tests at two-time points. The study found that advanced age, hypertension, large stroke size, higher stroke severity, and female sex were significant risk factors for poststroke cognitive dysfunction.
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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 | 0.002 |
| 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".