Abstract 13954: The Canadian Tia Score as a Predictor of Ischemic Lesion on Magnetic Resonance Imaging in Transient Ischemic Attack or Minor Stroke Following a Negative Computed Tomography Scan
Bibliographic record
Abstract
Introduction: Patients with a diagnosis of transient ischemic attack (TIA) or minor stroke with an acute infarction on brain imaging are at higher risk of subsequent stroke. Our goal was to establish if the Canadian TIA Score (CTS) could predict infarction on magnetic resonance imaging (MRI) when a computed tomography (CT) scan was negative for stroke and aimed to identify clinical factors predictive of a positive MRI. Methods: Patients were selected from the prospective cohort used for the validation of the CTS in 13 centers. Patients with negative CT scans who underwent MRI within 7 days were analyzed. The main outcome was cerebral infarction defined as MRI diffusion-weighted imaging (DWI) positivity. Associations between confirmed stroke and demographic characteristics, clinical features, laboratory findings, and medications were determined using a multivariate logistic regression model. Subsequent stroke rate at 7, 30, and 90 days was analyzed. Results: From 11,507 patients, 1,048 met inclusion criteria. MRI positivity was 15.4%, 30.4%, and 50.0% for the low, medium, and high-risk CTS groups, respectively. Subsequent stroke/TIA rates were higher with confirmed ischemic lesions on MRI at 90 days: twice (10.0%) in the low-risk group, 51 (22.3%) in the medium-risk group, and 20 (24.7%) in high-risk patients. 1.7% of DWI negative patients had a subsequent stroke. Predictive factors in multivariable models for DWI positivity in the medium-risk group were male (OR=1.53; 95% CI 1.11-2.12), hypertension (OR=1.63; 95% CI 1.17-2.27), clinical history of unilateral weakness (OR=2.09; 95% CI 1.50-2.91), language disturbance (OR=1.43; 95% CI 1.03-1.97), and the presence of pronator drift on examination (OR=2.18; 95% CI 1.37-3.47). Conclusion: The CTS helps predict MRI findings and confirmed ischemic lesion is a predictor of the recurrence risk of stroke. The low-risk group showed few positive MRIs and had a lower recurrence rate justifying less urgent MRI. In the medium-risk group, we highlighted findings that should raise the suspicion of an ischemic lesion and these patients should be prioritize for rapid investigation of stroke etiology. In the high-risk group, patients are at risk for stroke despite negative imaging and MRI should not delay management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".