Recurrent Events in Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The risk of a recurrent stroke after transient ischemic attack (TIA) or minor stroke is high. Clinical trials are needed to assess acute treatment options in these patients. We sought to evaluate the type of recurrent events and to identify which subsets of patients are at risk for recurrent events. METHODS: One hundred and eighty patients with TIA or minor stroke were examined within 12 hours and underwent brain MRI within 24 hours. Any neurological deterioration was recorded, and a combination of clinical and MRI factors were used to create a combined event classification. Subgroups of patients analyzed included classical TIA, patients with NIHSS=0, and patients with NIHSS >0 in ED. RESULTS: Overall there were 38 events in 36 patients (20% event rate); 20 were symptomatic and 18 were silent (only evident because of the follow up MRI). 18/20 (90%) symptomatic events were associated with progression of presenting symptoms, compared to 2/20 (10%) with a clear recurrent stroke distinct from the original event. We found a low risk of recurrent stroke among classical definition TIA patients (1.1%). Patients with an NIHSS=0 in the ED, had an intermediate event rate (6.6%) between TIA (classical - 1.1%) and NIHSS >0 (14.4%; chi(2) test for trend, P=0.02). All clinical categories of patient (TIA, stroke, NIHSS=0) accumulated silent lesions on MRI. CONCLUSIONS: Most events were classified as stroke progression or infarct growth rather than a recurrent stroke. A low risk of recurrence was found in patients with classical TIA and those with no neurological deficits on initial assessment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".