Cerebral Microbleeds and Functional Outcomes after Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Whether an association exists between cerebral microbleeds (CMBs) and functional recovery after ischemic stroke is unclear. We aimed to evaluate the association between CMBs and functional outcome after acute ischemic stroke. METHODS: Consecutive patients with acute stroke were enrolled, and all patients were stratified into good and poor functional outcome groups at discharge and 6 months after ischemic stroke by using a modified Rankin Scale score. Cardiovascular risk factors, CMBs, and white matter hyperintensities were compared between these two outcome groups. Logistic regression analysis was used to estimate the risk of poor functional outcomes. RESULTS: A total of 225 patients were enrolled, 121 of whom were classified as having a good functional outcome at discharge and 142 as having a good 6-month functional outcome. The presence of CMBs was associated with a poor functional outcome at discharge [CMBs (+) patients in poor vs. good functional group; 48.1% vs. 30.6%; p=0.007] and 6 months [53.0% vs. 30.3%; p=0.001]. After adjustment for confounding factors, only the presence of infratentorial CMBs was associated with a poor functional outcome at discharge and 6 months. The poor functional outcome group had more CMBs than the good outcome group at 6 months. CONCLUSIONS: Infratentorial cerebral microbleeds were significantly associated with worse functional outcomes not only in the early phase of ischemic stroke but also in the chronic phase. These findings suggest that the presence of infratentorial CMBs can predict poor functional outcome after acute ischemic stroke.
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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 | 0.003 |
| 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 | 0.000 | 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".