Abstract 117: A Province-wide Triaging System Improves Mortality After Transient Ischemic Attacks: The Alberta Stroke Prevention in TIAs and Mild Strokes (ASPIRE) Interventions
Bibliographic record
Abstract
Introduction: Previous work suggests that early evaluation and treatment may reduce the stroke risk after TIA by up to 80%. These models of care are resource intensive involving same day access to stroke prevention services, often in urban areas. This is not feasible in many settings, especially where large distances and rural populations are concerned. Hypothesis: We hypothesize that implementation of a province-wide, systematic, multifaceted intervention would lower the recurrent stroke rate in Alberta. Methods: This was a prospective quasi-experimental health services research in the province of Alberta involving a population of 4 million living in an area larger than France (660,000 km2). The ASPIRE interventions, implemented over 15 months, involved education to the public and healthcare providers, creation of a triaging algorithm based on clinical symptoms and onset time, and a 24-7 available TIA Hotline for rapid access to stroke expertise. The primary outcome was the 90-day stroke rate tested with an interrupted time-series regression analysis. Stroke outcomes were adjudicated by two stroke neurologists independently with discrepancies resolved by panel. Secondary outcomes were the composite of stroke, myocardial infarction, death, and the individual components from administrative data tested with age-sex adjusted logistic regression analysis. Results: We included 15709 TIA events in 13671 patients. Age-sex adjusted rate (and %) of stroke recurrence was 1.81 per 100,000 (1.85%) pre-implementation and 1.79 (1.65%) post. The primary outcome was neutral (autoregression coefficient 0.13, p-value 0.70). The 90-day mortality was significantly lower post-implementation (OR 0.75, 95%CI 0.60-0.94). There was a trend in decreased composite endpoint of stroke, myocardial infarction, and death (OR 0.88, 95%CI 0.77-1.01). Conclusions: In a population with low stroke recurrence rates, the successful province-wide implementation of the ASPIRE interventions was associated with decreased mortality, but did not significantly change stroke recurrence. Further studies on improving the identification of high-risk patients is necessary.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".