Visually-Rated Medial Temporal Lobe Atrophy with Lower Educational History as a Quick Indicator of Amnestic Cognitive Impairment after Stroke
Bibliographic record
Abstract
BACKGROUND: Time and resource limitations prevent cognitive assessment in acute-to-subacute settings, even in comprehensive stroke centers. OBJECTIVE: To assess cognitive function in acute stroke patients undergoing routine clinical, laboratory, and radiological investigations, with a view to improving post-stroke care and treatment. METHODS: Sixty-nine patients (72.6±11.1 years; 65% male) were prospectively enrolled within 14 days of acute ischemic stroke. Patients with altered consciousness, aphasia, or dysarthria were excluded. Clinical features including modified Rankin and NIH stroke scales, and vascular risk factors were assessed, as well as neuroimaging parameters by semi-quantitative evaluation of medial temporal lobe atrophy (MTLA) using MRA source images, FLAIR images for white matter changes (Fazekas scores), and T2∗ images for cerebral microbleeds. Neuropsychological screening was conducted using the Montreal Cognitive Assessment (MoCA) test. Univariate and multivariate analyses were used to evaluate the influence of variables on MoCA total and subscale scores. RESULTS: Lower MoCA scores of 22 or less were associated with MTLA [OR (95% CI), 5.3 (1.0-27.5); p = 0.045], education years [OR (95% CI), 0.71 (0.55-0.91); p = 0.007], and modified Rankin scale at discharge [OR (95% CI), 2.4 (1.3-4.5); p = 0.007]. The delayed recall MoCA score was correlated with MTLA (r = - 0.452, p < 0.001), periventricular (r = - 0.273, p = 0.024), and deep (r = - 0.242, p = 0.046), white matter changes. CONCLUSIONS: MTLA, together with lower educational history, are quick indicators of amnestic cognitive impairment after stroke. The association between cognitive impairment and physical disability at discharge may signify the importance of earlier cognitive assessment.
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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 | 0.000 |
| 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) | 0.002 | 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".