Early Diffusion Weighted MRI as a Negative Predictor for Disabling Stroke After ABCD <sup>2</sup> Score Risk Categorization in Transient Ischemic Attack Patients
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The prognostic value early diffusion-weighted magnetic resonance imaging (DWMRI) adds in the setting of transient ischemic attack (TIA), after risk stratification by a clinical score, is unclear. The purpose of this study is to evaluate, after ABCD2 score risk categorization in admitted TIA patients, whether negative DWMRI performed within 24 hours of symptom onset improves on the identification of patients at low risk for experiencing a disabling stroke within 90 days. METHODS: At 15 North Carolina hospitals, we enrolled a prospective nonconsecutive sample of admitted TIA patients. We excluded patients not undergoing a DWMRI within 24 hours of admission and patients for whom a dichotomized (< or = or >3) ABCD2 score could not be calculated. We conducted a medical record review to determine disabling ischemic stroke outcomes within 90 days. RESULTS: Over 35 months, 944 TIA patients met inclusion criteria, of whom 4% (n=41) had a disabling ischemic stroke within 90 days. In analyses stratified by low versus moderate/high ABCD2 score, the combination of a low risk ABCD2 score and a negative early DWMRI had excellent sensitivity (100%, 95% CI 34 to 100) for identifying low-risk patients. In patients classified as moderate to high risk, a negative early DWMRI predicted a low risk of disabling ischemic stroke within 90 days (sensitivity 92%, 95% CI 80 to 97; NLR 0.11, 95% CI 0.04 to 0.32). CONCLUSIONS: After risk stratification by the ABCD2 score, early DWMRI enhances the prediction of a low risk for disabling ischemic stroke within 90 days. Further study is warranted in a large, consecutive TIA population of early DWMRI as a sensitive negative predictor for disabling stroke within 90 days.
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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 | 0.001 | 0.001 |
| 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".