Leukoaraiosis and lacunes are associated with poor clinical outcomes in ischemic stroke patients treated with intravenous thrombolysis
Bibliographic record
Abstract
BACKGROUND: The effect of preexisting small vessel disease on outcomes of patients with ischemic stroke treated with i.v. thrombolysis is not fully understood. AIM: We aim to investigate the effect of combined leukoaraiosis and lacunes as detected on unenhanced brain computer tomography at baseline on clinical outcomes after i.v. thrombolysis. METHODS: We analyzed data from the Canadian Alteplase for Stroke Effectiveness Study. Small vessel disease was assessed on baseline computer tomography rating for leukoaraiosis and lacunes. We dichotomized the burden of small vessel disease to "absent or moderate" and "severe." Clinical outcomes at 90 days included excellent outcome (mRS = 0-1), good outcome (mRS = 0-2), and the occurrence of symptomatic intracerebral hemorrhage. Sensitivity analysis was performed on two age groups (≤80 versus >80). We ran logistic regression adjusting for confounders to evaluate independent effect of small vessel disease on outcomes. RESULTS: There were 820 patients with available brain computer tomography with mean age (±SD) of 71.3 (±13.2), 455 (55.5%) were male. Of these, 123 (15%) patients had severe small vessel disease at baseline. Age group analysis revealed significant associations of small vessel disease only in patients aged ≤80. After adjustment for confounders, presence of severe small vessel disease reduced the chances of both excellent (OR = 0.42, 95% CI = 0.24-0.74) and good outcome (OR = 0.35, 95% CI = 0.21-0.58) and with an increased risk of symptomatic intracerebral hemorrhage (OR = 5.91; 95% CI = 2.40-14.57). CONCLUSION: When considered together as radiological expressions of small vessel disease, presence and severity of severe leukoaraiosis and lacunes on baseline computer tomography scan are associated with poor clinical outcomes in patients treated with i.v. thrombolysis.
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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 | 0.005 |
| 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) | 0.001 | 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".