Shifting Trend of Transient Ischemic Attack Admission and Prognosis in Canada
Bibliographic record
Abstract
BACKGROUND: Stroke is often preceded by transient symptoms. Although global stroke rates have been shown to be declining, previous studies have reported inconsistent temporal trends of transient ischemic attacks (TIA). The objective of the current study is to report the temporal trends of TIA admissions and outcomes in Canada over the last 11 years. METHODS: We conducted a complete population cohort study using a national administrative database to study the temporal trend of age- and sex-adjusted TIA admission rates in Canada from 2003 to 2013. We also determined the rates of TIA and stroke diagnoses in the emergency department in the province of Ontario during the same period. We used multivariable analyses to study discharge location after acute hospitalization as well as 90-day stroke and/or TIA readmission rates. RESULTS: Of 425,799 admissions to an acute care hospital for all stroke and TIA, 71,443 (16.8%) were TIA. The age- and sex-standardized rates of TIA admission decreased significantly during the study period from 30.0 to 20.6 per 100,000 (p<0.0001). In Ontario, decreasing TIA admissions is mirrored by decreasing rates of TIA directly discharged from the emergency department (55.1 to 46.8 per 100,000, p = 0.002). The odds of 90-day readmission rates for stroke or TIA are also decreasing (adjusted odds ratio, 0.97; 95% confidence interval, 0.96-0.99). CONCLUSIONS: We show that TIA admission rates have declined in the past 11 years in Canada, reflecting improved vascular risk reduction and stroke care. Future studies to confirm our findings on improved stroke or TIA recurrence rates are 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".