Risk factors for decline in Montreal Cognitive Assessment (MoCA) scores in patients with acute transient ischemic attack and minor stroke
Bibliographic record
Abstract
Cognitive impairment after stroke/transient ischemic attack (TIA) has a high prevalence. The authors aimed to explore the risk factors for declined cognitive function with Montreal Cognitive Assessment (MoCA)-Beijing in patients with stroke/TIA at acute phase. Total 2283 patients with acute stroke/TIA without a history of dementia were assessed at 2 weeks of onset. Patients were assessed by MoCA-Beijing on day 14 and at 3 months follow-ups. Cognitive impairment was defined as MoCA-Beijing ≤22. Patients' cognitive status was considered as declined if there were a reduction of ≥2 points in MoCA-Beijing score and patients were considered to have improved if there were an increase of ≥2 points. The score of MoCA-Beijing was considered to be stable if there were an increase or decrease of 1 point. Most patients were in 60 s (60.96 ± 10.75 years old) with a median (interquartile range) National Institute of Health Stroke Scale score of 3.00 (4.00) and greater than primary school level of education, and 1657 participants (72.58%) were male. Cognitive evaluation was conducted in 2283 of 2625 patients (82.70%) with MoCA-Beijing at baseline. Total 292 (12.79%) patients have a cognitive decline at 3 months, 786 (34.42%) patients were stable and 1205 (52.78%) patients were improved. In the logistic regression, a history of hypertension was associated with cognitive deterioration from baseline to 3-month. Patients with a history of hypertension have a higher risk for cognitive deterioration from baseline to 3-month after stroke/TIA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".