Early stroke risk and ABCD2 score performance in tissue- vs time-defined TIA: A multicenter study
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".