Long-Term Risk of Stroke After Transient Ischemic Attack or Minor Stroke
Bibliographic record
Abstract
Importance: After a transient ischemic attack (TIA) or minor stroke, the long-term risk of stroke is not well-known. Objective: To determine the annual incidence rates and cumulative incidences of stroke up to 10 years after TIA or minor stroke. Data Sources: MEDLINE, Embase, and Web of Science were searched from inception through June 26, 2024. Study Selection: Prospective or retrospective cohort studies reporting stroke risk during a minimum follow-up of 1 year in patients with TIA or minor stroke. Data Extraction and Synthesis: Two reviewers independently performed data extraction and assessed study quality. Unpublished aggregate-level data on number of events and person-years during discrete follow-up intervals were obtained directly from the authors of the included studies to calculate incidence rates in individual studies. Data across studies were pooled using random-effects meta-analysis. Main Outcomes and Measures: The primary outcome was any stroke. Study-level characteristics were investigated as potential sources of variability in stroke rates across studies. Results: The analysis involved 171 068 patients (median age, 69 years [IQR, 65-71]; median proportion of male patients, 57% [IQR, 52%-60%]) from 38 included studies. The pooled rate of stroke per 100 person-years was 5.94 events (95% CI, 5.18-6.76; 38 studies; I2 = 97%) in the first year, 1.80 events (95% CI, 1.58-2.04; 25 studies; I2 = 90%) annually in the second through fifth years, and 1.72 events (95% CI, 1.31-2.18; 12 studies; I2 = 84%) annually in the sixth through tenth years. The 5- and 10-year cumulative incidence of stroke was 12.5% (95% CI, 11.0%-14.1%) and 19.8% (95% CI, 16.7%-23.1%), respectively. Stroke rates were higher in studies conducted in North America (rate ratio [RR], 1.43 [95% CI, 1.36-1.50]) and Asia (RR, 1.62 [95% CI, 1.52-1.73]), compared with Europe, in cohorts recruited in or after 2007 (RR, 1.42 [95% CI, 1.23-1.64]), and in studies that used active vs passive outcome ascertainment methods (RR, 1.11 [95% CI, 1.07-1.17]). Studies focusing solely on patients with TIA (RR, 0.68 [95% CI, 0.65-0.71) or first-ever index events (RR, 0.45 [95% CI, 0.42-0.49]) had lower stroke rates than studies with an unselected patient population. Conclusions and Relevance: Patients who have had a TIA or minor stroke are at a persistently high risk of subsequent stroke. Findings from this study underscore the need for improving long-term stroke prevention measures in this patient group.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".