A quantitative systematic review of domain-specific cognitive impairment in lacunar stroke
Bibliographic record
Abstract
OBJECTIVE: To quantitatively characterize domain-specific cognition in individuals with symptomatic lacunar stroke in a systematic review. METHODS: Systematic searches of MEDLINE and EMBASE were conducted. Inclusion criteria were all articles published prior to December 2011 evaluating domain-specific cognitive status in individuals with a symptomatic lacunar infarct. Data extraction identified cognitive domains with reported impairment and effect size calculations and heterogeneity analyses were completed to assess the magnitude of this impairment for all studies with control group data. RESULTS: Results of the search yielded 12 cross-sectional and 5 longitudinal studies that met inclusion criteria. Effect size calculations revealed small to medium effect sizes (ES) estimations for impairment after stroke in the domains of executive function (ES -0.44, 95% confidence interval [CI] -0.83, -0.50), memory (ES -0.55, 95% CI -0.96, -0.13), language (ES -0.63, 95% CI -0.92, -0.33), attention (ES -0.37, 95% CI -0.67, -0.07), and visuospatial abilities (ES -0.61, 95% CI -1.03, 0.19), and large effect sizes for global cognition (ES -0.90, 95% CI -1.48, -0.31) and information processing speed (ES -0.93, 95% CI -1.63, -0.23). Heterogeneity analyses revealed that a subset of these domains were heterogeneous and identified moderating factors accounting for this heterogeneity. CONCLUSIONS: Results of this systematic review are consistent with previous characterizations of cognitive impairment associated with lacunar strokes. However, impaired cognition in this stroke subtype appears less selective than previously thought, involving all major cognitive domains.
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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 | 0.030 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".