Association of White Matter Hyperintensities With Short-Term Outcomes in Patients With Minor Cerebrovascular Events
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: White matter lesions (WML) are associated with cognitive decline, increased stroke risk, and disability in old age. We hypothesized that superimposed acute cerebrovascular occlusion on chronic preexisting injury (leukoaraiosis) leads to worse outcome after minor cerebrovascular event, both using quantitative (volumetric) and qualitative (Fazekas scale) assessment, as well as relative total brain volume. METHODS: WML volume assessment was performed in 425 patients with high-risk transient ischemic attack (TIA; motor/speech deficits >5 minutes) or minor strokes from the CATCH study (CT and MRI in the Triage of TIA and Minor Cerebrovascular Events to Identify High Risk Patients). Complete baseline characteristics and outcome assessment were available in 412 patients. Primary outcome was disability at 90 days, defined as modified Rankin Scale score of >1. Secondary outcomes were stroke progression, TIA recurrence, and stroke recurrence. Analysis was performed using descriptive statistics and regression models including interaction terms. RESULTS: =0.79). Both higher Fazekas score and higher WMH volume were associated with disability at 90 days in univariate regression (odds ratio 1.22; 95% confidence interval, 1.04-1.43 and odds ratio, 1.25 per milliliter increase; 95% confidence interval, 1.02-1.54, respectively) but not with stroke progression, TIA recurrence, or stroke recurrence. In multivariable-adjusted analyses, additive interaction terms were associated with unfavorable outcome (adjusted odds ratio 3.99, 95% confidence interval, 1.87-8.49). CONCLUSIONS: Our data suggest that quantitative and qualitative WML assessments are highly correlated and comparable in TIA/minor stroke patients. WML burden is associated with short-term outcome of patients with good prestroke function in the presence of intracranial stenosis/occlusion.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".