Long-Term Outcomes of Ischemic Stroke of Undetermined Mechanism: A Population-Based Prospective Cohort
Bibliographic record
Abstract
<b><i>Background and Purpose:</i></b> Little is known about the short- and long-term outcomes of ischemic stroke of undetermined mechanism (ISUM). <b><i>Methods:</i></b> Subjects were recruited from the Mashhad Stroke Incidence Study. Ischemic stroke (IS) was classified on the basis of the TOAST criteria. We further categorized patients with ISUM into ISUM<sub>neg</sub> (negative clinical/test results for large artery, small artery) and ISUM<sub>inc</sub> (incomplete investigations). Cox proportional hazard models and the competing-risk regression model were used to compare 1 and 5 years mortality (all-causes) and recurrent rate among IS subtypes. <b><i>Results:</i></b> Overall, 1-year mortality was higher in those with ISUM<sub>inc</sub> than in ISUM<sub>neg</sub> (adjusted hazard ratio [aHR] 1.6, 95% CI 1.01–2.8; <i>p</i> = 0.04) and in other stroke subtypes. Cardioembolic stroke was associated with the greatest risk of stroke recurrence at one year (aHR 4.9, 95% CI 1.8–12.9; <i>p</i> = 0.001) and 5 years (HR 2.1, 95% CI 1.1–3.7; <i>p</i> = 0.01) as compared to ISUM<sub>neg</sub>. <b><i>Conclusions:</i></b> The classification of ISUM as a single group may lead to over- or underestimation of mortality and recurrence in this major category of IS. A better definition of ISUM is necessary to predict death and recurrence accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".