Reduction in Stroke After Transient Ischemic Attack in a Province-Wide Cohort Between 2003 and 2015
Bibliographic record
Abstract
BACKGROUND: Improvements in management of transient ischemic attack (TIA) have decreased stroke and mortality post-TIA. Studies examining trends over time on a provincial level are limited. We analyzed whether efforts to improve management have decreased the rate of stroke and mortality after TIA from 2003 to 2015 across an entire province. METHODS: Using administrative data from the Canadian Institute for Health Information's (CIHI) databases from 2003 to 2015, we identified a cohort of patients with a diagnosis of TIA upon discharge from the emergency department (ED). We examined stroke rates at Day 1, 2, 7, 30, 90, 180, and 365 post-TIA and 1-year mortality rates and compared trends over time between 2003 and 2015. RESULTS: From 2003 to 2015 in Ontario, there were 61,710 patients with an ED diagnosis of TIA. Linear regressions of stroke after the index TIA showed a significant decline between 2003 and 2015, decreasing by 25% at Day 180 and 32% at 1 year (p < 0.01). The 1-year stroke rate decreased from 6.0% in 2003 to 3.4% in 2015. Early (within 48 h) stroke after TIA continued to represent approximately half of the 1-year event rates. The 1-year mortality rate after ED discharge following a TIA decreased from 1.3% in 2003 to 0.3% in 2015 (p < 0.001). INTERPRETATION: At a province-wide level, 1-year rates of stroke and mortality after TIA have declined significantly between 2003 and 2015, suggesting that efforts to improve management may have contributed toward the decline in long-term risk of stroke and mortality. Continued efforts are needed to further reduce the immediate risk of stroke following a TIA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".