Early stroke risk and ABCD2 score performance in tissue- vs time-defined TIA
Bibliographic record
Abstract
OBJECTIVES: Stroke risk immediately after TIA defined by time-based criteria is high, and prognostic scores (ABCD2 and ABCD3-I) have been developed to assist management. The American Stroke Association has proposed changing the criteria for the distinction between TIA and stroke from time-based to tissue-based. Research using these definitions is lacking. In a multicenter observational cohort study, we have investigated prognosis and performance of the ABCD2 score in TIA, subcategorized as tissue-positive or tissue-negative on diffusion-weighted imaging (DWI) or CT imaging according to the newly proposed criteria. METHODS: Twelve centers provided data on ABCD2 scores, DWI or CT brain imaging, and follow-up in cohorts of patients with TIA diagnosed by time-based criteria. Stroke rates at 7 and 90 days were studied in relation to tissue-positive or tissue-negative subcategorization, according to the presence or absence of brain infarction. The predictive power of the ABCD2 score was determined using area under receiver operator characteristic curve (AUC) analyses. RESULTS: A total of 4,574 patients were included. Among DWI patients (n = 3,206), recurrent stroke rates at 7 days were 7.1%(95% confidence interval 5.5-9.1) after tissue-positive and 0.4% (0.2-0.7) after tissue-negative events (p diff < 0.0001). Corresponding rates in CT-imaged patients were 12.8% (9.3-17.4) and 3.0% (2.0-4.2), respectively (p diff < 0.0001). The ABCD2 score had predictive value in tissue-positive and tissue-negative events (AUC = 0.68 [95% confidence interval 0.63-0.73] and 0.73 [0.67-0.80], respectively; p sig < 0.0001 for both results, p diff = 0.17). Tissue-positive events with low ABCD2 scores and tissue-negative events with high ABCD2 scores had similar stroke risks, especially after a 90-day follow-up. CONCLUSIONS: Our findings support the concept of a tissue-based definition of TIA and stroke, at least on prognostic grounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".