An Improved Scoring System for Identifying Patients at High Early Risk of Stroke and Functional Impairment after an Acute Transient Ischemic Attack or Minor Stroke
Bibliographic record
Abstract
BACKGROUND: Risk of a subsequent stroke following an acute transient ischemic attack (TIA) or minor stroke is high. The ABCD(2) tool was proposed as a method to triage these patients using five clinical factors. Modern imaging of the brain was not included. The present study quantified the added value of magnetic resonance imaging (MRI) factors to the ABCD(2) tool. METHODS: Patients with TIA or minor stroke were examined within 12 h and had a brain MRI within 24 h of symptom onset. Primary outcomes were recurrent stroke and functional impairment at 90 days. A new tool, ABCD(2)+MRI, was created by adding diffusion-weighted imaging lesion and vessel occlusion status to the ABCD(2) tool. The predictive accuracy of both tools was quantified by the area under the curve (AUC). RESULTS: One hundred and eighty patients were enrolled and 11.1% had a recurrent stroke within 90 days. The predictive accuracy of the ABCD(2)+MRI was significantly higher than ABCD(2) (AUC of 0.88 vs. 0.78, P=0.01). Those with a high score (7-9) had a 90-day recurrent stroke risk of 32.1%, moderate score (5-6) risk of 5.4%, and low score (0-4) risk of 0.0%. The ABCD(2) tool did not predict risk of functional impairment at 90 days (P=0.33), unlike the ABCD(2)+MRI (P=0.02): high score (22.9%), moderate (7.5%), low (7.7%). CONCLUSIONS: Risk of recurrent stroke and functional impairment after a TIA or minor stroke can be accurately predicted by a scoring system that utilizes both clinical and MRI information. The ABCD(2)+MRI score is simple and its components are commonly available during the time of admission.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".