Hemodynamic correlates of transient cognitive impairment after transient ischemic attack and minor stroke: A transcranial Doppler study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Transient cognitive impairment (TCI) on the Mini Mental State Evaluation score is common after transient ischemic attack/minor stroke and might identify patients at increased risk of dementia. We aimed to replicate TCI using the Montreal Cognitive Assessment (MoCA), compare it with persistent Mild Cognitive Impairment (PMCI), and to determine whether global cerebral hemodynamic changes could explain transient impairment. METHODS: Consecutive patients with transient ischemic attack/minor stroke (NIHSS ≤ 3) were assessed with the MoCA and transcranial Doppler ultrasound acutely and at 1 month. We compared patients with TCI (baseline MoCA < 26 with ≥ 2 points increase at 1 month), PMCI (MoCA < 26 with < 2 points increase), and no cognitive impairment (NCI; MoCA ≥ 26). RESULTS: Of 326 patients, 46 (14.1%) had PMCI, 98 (30.1%) TCI, and 182 (55.8%) NCI. At baseline, TCI patients had higher systolic blood pressure (150.95 ± 21.52 vs 144.86 ± 22.44 mmHg, p = 0.02) and lower cerebral blood flow velocities, particularly end-diastolic velocity (30.16 ± 9.63 vs 35.02 ± 9.01 cm/s, p < 0.001) and mean flow velocity (48.95 ± 12.72 vs 54 ± 12.46 cm/s, p = 0.001) than those with NCI, but similar clinical and hemodynamic profiles to those with PMCI. Systolic BP fell between baseline and 1 month (mean reduction = 14.01 ± 21.26 mmHg) and end-diastolic velocity and mean flow velocity increased (mean increase = + 2.42 ± 6.41 and 1.89 ± 8.77 cm/s, respectively), but these changes did not differ between patients with TCI, PMCI, and NCI. CONCLUSIONS: TCI is detectable with the MoCA after transient ischemic attack and minor stroke and has similar clinical and hemodynamic profile to PMCI. However, TCI does not appear to be due to exaggerated acute reversible global hemodynamic changes.
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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 | 0.000 | 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".