Computed Tomography and Computed Tomography Angiography Findings Predict Functional Impairment in Patients with Minor Stroke and Transient Ischaemic Attack
Bibliographic record
Abstract
INTRODUCTION: Abnormalities on acute magnetic resonance imaging predict outcome in minor stroke and transient ischaemic attack patients. We hypothesised that noncontrast computed tomography and computed tomography angiography findings in minor stroke and transient ischaemic attack patients would also predict functional outcome. METHODS: We analysed consecutive patients with a transient ischaemic attack or a minor stroke with an National Institute of Health Stroke Scale or=2 ) at 90 days. RESULTS: Among 457 patients, the median baseline National Institute of Health Stroke Scale score was 1. Median time from symptom onset to noncontrast computed tomography was 278 min (interquartile range 151-505) and median delay from noncontrast computed tomography to CT angiography was 3 min (interquartile range 0-13). At 90 days, 57 patients (12.5%) had a mRS >or=2. Clinical factors that were associated with functional impairment were age >or=60 years (RR 2.05 CI(95) 1.16-3.64) and baseline National Institute of Health Stroke Scale score >0 (RR 3.23 1.72-6.06). All the assessed computed tomography parameters (acute stroke on noncontrast computed tomography and intracranial or extracranial stenosis or occlusion) were individually predictive of functional impairment. A composite computed tomography imaging 'at risk' metric, defined by acute stroke on noncontrast computed tomography, Circle of Willis intracranial vessel occlusion or >or=50% stenosis, extracranial occlusion or >or=50% stenosis, was associated with poorer outcome (RR 2.92 CI(95) 1.81-4.71). CONCLUSIONS: The presence of an acute stroke on noncontrast computed tomography or an intracranial or extracranial occlusion or stenosis was associated with an increased risk of functional impairment. Multi-modal computed tomography could be used to identify high-risk transient ischaemic attack or minor stroke patients.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".