Predicting 1-Year Stroke Recurrence and Mortality in Stable Outpatients Following Ischemic Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND: Patients with stroke or transient ischemic attack (TIA) are at high early risk of mortality and morbidity. Current risk prediction tools focus on patients after hospital discharge but not on those surviving to outpatient follow-up. We examined whether demographic and medical history data could predict 1-year stroke recurrence and mortality, among those discharged alive and event-free for 90 days after stroke and 1 day after TIA. METHODS: Data were obtained from the Ontario Stroke Registry (13,848 stroke and 13,059 TIA patients) and linked to administrative databases. Two-thirds of each cohort were used for model derivation and one-third for validation. Multivariable regression models were used to predict stroke recurrence and all-cause mortality. RESULTS: There were 238 (2.71%) recurrent strokes in the ischemic stroke and 298 (3.44%) in the TIA cohorts at one year. Increasing age and previous stroke/TIA were associated with an increased risk of recurrent stroke in both cohorts. A higher modified Rankin Scale and diabetes were associated with an increased risk of recurrent stroke in the stroke cohort and heart failure, smoking and discharge location in the TIA cohort. Time-dependent areas under the curve were modest, 0.59 (0.54-0.64) and 0.59 (0.55-0.64) for the stroke and TIA validation cohorts, respectively. C-statistics from derivation and validation cohorts for mortality ranged from 0.74-0.78. CONCLUSION: The predictive accuracy of the models was quite low after accounting for several risk factors. Additional risk factors associated with stroke recurrence for people seen in outpatient stroke clinics, and innovative approaches to individualized secondary prevention are needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".