Post TIA care in Ontario: A population-based cohort study on secondary prevention
Bibliographic record
Abstract
BACKGROUND: Transient ischemic attacks (TIAs) often precede ischemic strokes. Little is known about adherence to secondary prevention guidelines among individuals post TIA. METHODS: We conducted a population-level, retrospective cohort study of individuals in Ontario, Canada, with a TIA between April 2010 to March 2019, that survived at least one year. We assessed low density lipoprotein (LDL) and glycated hemoglobin (HbA1C) testing, receipt of important secondary prevention medications for individuals aged 65 years and older, and receipt of influenza vaccines. Health care utilization was described in the 90 days following the TIA. We compared these rates to a cohort of individuals after first ischemic stroke. RESULTS: After exclusions, 36,487 individuals were included (mean age 68.6 years; 50.0 % female). LDL testing was performed in 66.3 % of individuals, and 58.4 % had an HbA1C test in the year following their TIA. Lipid lowering medications were prescribed to 75.2 %, while 82.7 % received an antihypertensive. Among individuals with diabetes, 68.6 % were prescribed an anti-hyperglycemic, and among those with atrial fibrillation, 81.1 % were prescribed an anticoagulant. Influenza vaccines were administered to 43.6 % of individuals. Within 90 days, 30.2 % visited an emergency department, and 94.2 % saw a primary care provider. Results were similar when individuals were followed for three years post TIA. Compared to ischemic stroke survivors, individuals post-TIA were less likely to have an HbA1C test (p-value <0.001) or receive important secondary prevention medications, but were more likely to receive an influenza vaccine (p-value <0.001). CONCLUSIONS: Observance of secondary prevention guidelines following a TIA could be improved for several clinical recommendations, especially when compared to ischemic stroke survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".