Serial Montreal Cognitive Assessments Demonstrate Reversible Cognitive Impairment in Patients With Acute Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive changes after ischemic stroke are often overlooked, particularly acutely and in patients with mild or transient deficits. We assessed patients with transient ischemic attack (TIA)/minor stroke with serial cognitive screening tests. We tested the hypothesis that mild acute deficits are transient and improve after TIA/minor stroke. METHODS: Patients with acute TIA/minor ischemic stroke, without a history of cognitive impairment, presenting with a National Institute of Health Stroke Scale score ≤3 were assessed <72 hours of onset. Patients were administered the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) at days 1, 7, 30, and 90. Cognitive impairment was defined as MoCA <26 and MMSE ≤26. RESULTS: One hundred patients with a median (interquartile range) National Institute of Health Stroke Scale score of 1 (2) and median age of 68 (20) years were included. Baseline median MoCA score (26 [4]) was lower than the MMSE (29 [2]; P<0.0001). Cognitive impairment was detected in 54 of 100 patients (54%) with MoCA and 16 of 100 (16%; P=0.001) with MMSE. MoCA scores improved at day 7 (27 [5]), day 30 (28 [2]), and day 90 (28 [2]; P<0.0001). Resolution of cognitive deficits was because of resolution of recall deficits. CONCLUSIONS: Acute temporary cognitive impairment after TIA/minor stroke is common. The MoCA is sensitive to these changes, but the MMSE is not. Routine cognitive assessment after TIA/minor stroke may be warranted and relevant to return to activities even when other neurological deficits are not evident.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".