Organized Outpatient Care of Patients with Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
Abstract The risk of recurrent stroke after transient ischemic attack (TIA) is high. In the past 10 years, TIA has increasingly been recognized as a medical emergency. Health systems have adapted toward rapid evaluation, investigation, and secondary prevention in patients with presumed TIA and minor stroke, and the significant benefits in reducing recurrent stroke and mortality have been borne out in several landmark studies. Various scores have been developed and debated to better risk stratify patients with TIA for hospitalization or urgent referral. However, scoring systems face challenges in identifying all patients with high-risk etiologies such as atrial fibrillation and carotid stenosis, and therefore require further refinement before widespread use. Further challenges include the role of advanced imaging in TIA, and ensuring rapid access to specialist care for all patients. In the absence of definitive risk stratification methods, the authors conclude that all patients with suspected TIA and minor stroke should be assessed and treated on an urgent basis, ideally through rapid outpatient referral programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".