CT perfusion identified potential treatment opportunities in one in five mild strokes
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".