CT perfusion identified potential treatment opportunities in one in five mild strokes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Guidelines generally advise against reperfusion therapy in patients with mild stroke (NIHSS ≤ 5) and non-disabling symptoms. However, stroke severity can fluctuate, and clinical scores may not fully capture tissue at risk. Reliance on non-contrast CT (NCCT), potentially missing perfusion deficits or large vessel occlusions (LVOs). Advanced imaging-including CT angiography (CTA) and CT perfusion (CTP)-can reveal significant hypoperfusion in otherwise mild presentations. This study aimed to quantify the proportion of increased tissue-at-risk volumes (Tmax + 6s ≥ 15 mL) in patients with mild acute ischaemic stroke and identify associated factors and outcomes. METHODS: We included consecutive AIS patients within 24 h of onset from multicentre stroke registries in Australia and Indonesia. Only those with baseline NCCT, CTA, and CTP were analysed. Patients were stratified into NIHSS ≤ 5 and > 5. Tissue-at-risk was defined as Tmax + 6s ≥ 15 mL. Clinical, imaging, and outcome data were compared, and predictors of poor functional outcome (mRS 3-6 at 90-day) were assessed. RESULTS: Of 655 patients, 314 had NIHSS ≤ 5. Among these, 22.9% exhibited Tmax + 6s ≥ 15 mL, indicating significant hypoperfusion. This subgroup had worse 90-day outcomes (26.4% mRS 3-6 vs. 9.5%, p < 0.001). Tmax + 6s ≥ 15 mL, hypertension, and LVO were independently associated with poor outcome (adjusted ORs: 2.51, 3.15, and 2.74 respectively). ROC analysis demonstrated moderate discrimination of Tmax + 6s volume for poor functional outcome. CONCLUSIONS: A substantial proportion of mild stroke patients harbour treatable perfusion deficits. CT perfusion provides essential prognostic information beyond clinical severity, supporting its role in guiding therapeutic decisions-even in low NIHSS presentations where standard imaging may otherwise overlook tissue at risk.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it