The high risk of stroke immediately after transient ischemic attack
Bibliographic record
Abstract
BACKGROUND: The risk of stroke is elevated in the first 48 hours after TIA. Previous prognostic models suggest that diabetes mellitus, age, and clinical symptomatology predict stroke. The authors evaluated the magnitude of risk of stroke and predictors of stroke after TIA in an entire population over time. METHODS: Administrative data from four different databases were used to define TIA and stroke for the entire province of Alberta for the fiscal year (April 1999-March 2000). The age-adjusted incidence of TIA was estimated using direct standardization to the 1996 Canadian population. The risk of stroke after a diagnosis of TIA in an Alberta emergency room was defined using a Kaplan-Meier survival function. Cox proportional hazards modeling was used to develop adjusted risk estimates. Risk assessment began 24 hours after presentation and therefore the risk of stroke in the first few hours after TIA is not captured by our approach. RESULTS: TIA was reported among 2,285 patients for an emergency room diagnosed, age-adjusted incidence of 68.2 per 100,000 population (95% CI 65.3 to 70.9). The risk of stroke after TIA was 9.5% (95% CI 8.3 to 10.7) at 90 days and 14.5% (95% CI 12.8 to 16.2) at 1 year. The risk of combined stroke, myocardial infarction, or death was 21.8% (95% CI 20.0 to 23.6) at 1 year. Hypertension, diabetes mellitus, and older age predicted stroke at 1 year but not earlier. CONCLUSIONS: Although stroke is common after TIA, the early risk is not predicted by clinical and demographic factors. Validated models to identify which patients require urgent intervention are needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".