Stratified, Urgent Care for Transient Ischemic Attack Results in Low Stroke Rates
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transient ischemic attack (TIA) is a marker for early risk of stroke. No previous studies have assessed the use of urgent stroke prevention clinics for emergency department (ED) patients with TIA. We hypothesized that an ABCD2-based ED triaging tool for TIA with outpatient management would be associated with lower 90-day stroke rate than that predicted by ABCD2. METHODS: A cohort of prospectively identified patients presenting with symptoms suggestive of TIA seen in 2 tertiary-care EDs. These patients were divided into 3 strata based on their ACBD2 score, and triage targets were set for each stratum. All patients received the same standard of care in the Stroke Clinic regardless of their risk score. Primary outcome was stroke by 90 days of index TIA. Secondary outcomes were subsequent TIA, myocardial infarction, or death. RESULTS: One-thousand ninety-three patients met the inclusion criteria; 982 patients completed 90-day follow-up and comprised the final cohort. After stratification, 32%, 49%, and 19% of patients were categorized as low-, moderate-, or high-risk, respectively. The overall 90-day risk of stroke in all patients was 3.2%, compared with the ABCD2-predicted risk of 9.2%. Only 1.6% of patients with TIA/minor stroke were admitted from the ED. The risk of subsequent TIA, myocardial infarction, or death by 90 days was 5.5%, 0.1%, and 1.7%, respectively. CONCLUSIONS: Outpatient care in a rapid-access stroke prevention clinic using the ABCD2 score for triage resulted in a low 90-day stroke rate for patients in the ED with TIA. Benefit occurred without requiring admission for most patients.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".