Cognitive performance and aphasia recovery
Bibliographic record
Abstract
Objectives This study assessed cognitive performance of subjects with aphasia during the acute stage of stroke and evaluated how such performance relates to recovery at 3 months. Materials & methods Patients with aphasia following a left hemisphere stroke were evaluated during the first (baseline) and the fourth-month post onset. Assessment comprised non-verbal tests of attention/processing speed (Symbol Search, Cancelation Task), executive functioning (Matrix Reasoning, Tower of Hanoi, Clock Drawing, Motor Initiative), semantic (Camel and Cactus Test), episodic and immediate memory (Memory for Faces Test, 5 Objects Memory Test, and Spatial Span. Recovery was measured by the Token Test score at 3 months. The impact of baseline performance on recovery was evaluated by logistic regression adjusting for age, education, severity of aphasia and the Alberta Stroke Program Early CT (ASPECT) score. Results Thirty-nine subjects (with a mean of 66.5 ± 10.6 years of age, 17 men) were included. Average baseline cognitive performance was within normal range in all tests except in memory tests (semantic, episodic and immediate memory) for which scores were ≤-1.5sd. Subjects with poor aphasia recovery (N = 27) were older and had fewer years of formal education but had identical ASPECT score compared to those with favorable recovery. Considering each test individually, the score obtained on the Matrix Reasoning test was the only one to predict aphasia recovery (Exp(B)=24.085 p = 0.038). Conclusions The Matrix Reasoning Test may contribute to predict aphasia recovery. Cognitive performance is a measure of network disruption but may also indicate the availability of recovery strategies.
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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 | 0.003 |
| 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) | 0.001 | 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".