Leukoaraiosis and intracerebral hemorrhage after thrombolysis in acute stroke
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether the presence of leukoaraiosis or multiple lacunes is associated with symptomatic intracerebral hemorrhage (ICH) and 90-day outcome after thrombolytic treatment with tissue plasminogen activator (tPA). METHODS: Data were from a Canadian national registry of thrombolyzed patients with ischemic stroke. A total of 820 scans were assessed, blind to clinical features, for the presence of severe vs no/moderate leukoaraiosis, and of multiple (>2) vs no/single lacunar infarcts. Logistic regression was used to determine if an independent interaction existed between the presence and degree of leukoaraiosis/lacunes and risk of symptomatic ICH, and to evaluate the predictive role of leukoaraiosis and lacunes in relation to 90-day outcome. RESULTS: An overall symptomatic ICH rate of 3.5% was observed. The rate of symptomatic ICH increased up to 10% in patients with severe leukoaraiosis and multiple lacunes. A significant association was observed between ICH risk and either severe leukoaraiosis (RR = 2.7 [95% CI 1.1 to 6.5]) or multiple lacunes (RR = 3.4 [95% CI 1.5 to 7.6]). Patients with multiple lacunes, but not leukoaraiosis, had higher mortality at 90 days compared to those with one or no lacunes (OR = 2.9, 95% CI 1.3 to 6.2, p = 0.008). No difference was observed in the good outcome rate among patients with and without leukoaraiosis or lacunes or both. CONCLUSION: The presence of small vessel disease on CT scan does not affect overall clinical outcome at 3 months in routine community use of tPA for ischemic stroke. A significant increase in the risk of symptomatic ICH is observed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".