What Causes Disability After Transient Ischemic Attack and Minor Stroke?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Minor stroke and transient ischemic attack portend a significant risk of disability. Three possible mechanisms for this include disability not captured by the National Institutes of Health Stroke Scale, symptom progression, or recurrent stroke. We sought to assess the relative impact of these mechanisms on disability in a population of patients with transient ischemic attack and minor stroke. METHODS: Five hundred ten consecutive minor stroke (National Institutes of Health Stroke Scale<4) or patients with transient ischemic attack who were previously not disabled and had a CT/CT angiography completed within 24 hours of symptom onset were prospectively enrolled. Disability was assessed at 90 days using the modified Rankin Scale. Predictors of disability (modified Rankin Scale≥2) and the relative impact of the initial event versus recurrent events were assessed. RESULTS: Seventy-four of 499 (15%; 95% CI, 12%-18%) patients had a disabled outcome. Baseline factors predicting disability were: age≥60 years, diabetes mellitus, premorbid modified Rankin Scale 1, ongoing symptoms, baseline National Institutes of Health Stroke Scale, CT/CT angiography-positive metric, and diffusion-weighted imaging positivity. In the multivariable analysis ongoing symptoms (OR, 2.4; 95% CI, 1.3-4.4; P=0.004), diabetes mellitus (OR, 2.3; 95% CI, 1.2-4.3; P=0.009), female sex (OR, 1.8; 95% CI, 1.1-3; P=0.025), and CT/CT angiography-positive metric (OR, 2.4; 95% CI, 1.4-4; P=0.001) predicted disability. Of the 463 patients who did not have a recurrent event, 55 were disabled (12%). By contrast 19 of 36 (53%) patients were disabled after a recurrent event (risk ratio, 4.4; 95% CI, 3-6.6; P<0.0001). CONCLUSIONS: We found that a substantial proportion of patients with transient ischemic attack and minor stroke become disabled. In terms of absolute numbers, most patients have disability as a result of their presenting event; however, recurrent events have the largest relative impact on outcome.
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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) | 0.000 | 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 | 0.000 | 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 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".