The ASPIRE Approach for TIA Risk Stratification
Bibliographic record
Abstract
BACKGROUND: The risk of stroke after transient ischemic attack (TIA) is elevated in the days to weeks after TIA. A variety of prediction rules to predict stroke risk have been suggested. In Alberta a triage algorithm to facilitate urgent access based on risk level was agreed upon for the province. Patients with ABCD2 score ≥ 4, or motor or speech symptoms lasting greater than five minutes, or with atrial fibrillation were considered high risk (the ASPIRE approach). We assessed the ability of the ASPIRE approach to identify patients at risk for stroke. METHODS: We retrospectively reviewed charts from 573 consecutive patients diagnosed with TIA in Foothills Hospital emergency room from 2002 through 2005. We recorded clinical and event details and identified the risk of stroke at three months. RESULTS: Among 573 patients the 90-day risk of stroke was 4.7% (95% CI 3.0%, 6.4%). 78% of the patients were identified as high risk using this approach. In patients defined as high risk on the ASPIRE approach there was a 6.3% (95% CI 4.2%, 8.9%) risk of stroke. In patients defined as low risk using the ASPIRE approach there were no recurrent strokes (100% negative predictive value). In contrast, two patients with low ABCD2 scores (ABCD2 score < 4) suffered recurrent strokes. CONCLUSION: The ASPIRE approach has a perfect negative predictive value in the population in predicting stroke. However, this high sensitivity comes at a cost of identifying most patients as high risk.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".