Incidence and Etiology of Microinfarcts in Patients with Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cerebral microinfarcts (CMI) are associated with intracerebral hemorrhage due to small vessel disease (SVD) in studies not including an ischemic etiologic workup. We aimed to determine their incidence and potential causes in a large ischemic stroke (IS) cohort. METHODS: Consecutive patients with MRI-confirmed IS within 72 hours of onset were enrolled. Subjects had either single high-risk embolic source (cardioembolic or large vessel disease) or no embolic source. CMIs were classified by their relationship to the primary infarct as within or outside the same vascular territory. White matter hyperintensities (WMH) and microbleeds were markers SVD severity. Multivariable regression tested the association between CMIs and potential etiologies. RESULTS: We analyzed 946 IS patients, mean age 69 ± 15 years, 46% female. We detected CMI (≤5 mm) on diffusion-weighted imaging in 269 (28%) subjects, 190 (71%) within the vascular territory of the primary infarct. Large-vessel atherosclerosis (P <.001), cardioembolic source (P <.001), higher WMH (P = .032) and lower systolic blood pressure (SBP, P = .024) were independently associated with the presence of CMI. While SBP was associated with CMI in any location (P <.05), WMH was only associated with CMI outside the territory of the primary infarct (P = .033), and large vessel atherosclerosis with CMI within the primary infarct territory (P = .004). CONCLUSIONS: CMIs occurring within the vascular territory of a larger infarct are more likely embolic, but those occurring outside are probably related to SVD. Our findings suggest a role for SVD in pathogenesis of CMIs and emphasize the importance of etiologic workup to identify alternate etiologies.
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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 | 0.001 | 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".