Importance of Leukoaraiosis on CT for Tissue Plasminogen Activator Decision Making: Evaluation of the NINDS rt-PA Stroke Study
Bibliographic record
Abstract
BACKGROUND: Leukoaraiosis is associated with microhemorrhages on T(2)*-weighted magnetic resonance imaging of the brain. Such hemorrhages have been postulated to be responsible for symptomatic intracerebral hemorrhage (ICH) after thrombolytic treatment. We examined the relationship between small-vessel ischemic disease and symptomatic ICH within the NINDS rt-PA Stroke Study. METHODS: Baseline CT scans from the NINDS rt-PA Stroke Study were re-evaluated retrospectively by blinded expert CT readers using the van Swieten Score (vSS) for leukoaraiosis. The scale examined the severity of white-matter changes on 3 serial CT slices and graded separately for the 2 distinct regions anterior and posterior to the central sulcus: 0 = no lesion, 1 = partly involving the white matter, and 2 = extending up to the cortex. RESULTS: 603 CT scans were interpreted. The risk of symptomatic ICH increased with higher vSS in both the placebo and treatment groups. The absolute risk of symptomatic hemorrhage was 7.9% in the rt-PA-treated cohort among patients with severe white-matter disease (vSS = 3-4) versus 2.9% receiving placebo. Among severe leukoaraiosis patients (vSS = 3-4), no differential treatment effect was seen with rt-PA patients achieving better outcomes than placebo, modified Rankin score 0-1 in 31.6% of rt-PA-treated versus 14.7% of placebo-treated patients. CONCLUSION: The results from the present study do not support the concept that leukoaraiosis present on baseline noncontrast CT scanning is critical to thrombolysis decision making in the first 3 h from symptom onset. No clear leukoaraiosis threshold was identified below which no benefit or harm could be seen from intravenous rt-PA therapy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".