Long-Term Risk of Recurrent Stroke in Young Cryptogenic Stroke Patients with and without Patent Foramen Ovale
Bibliographic record
Abstract
BACKGROUND: Among patients with a patent foramen ovale and cryptogenic ischemic stroke, the long-term prognosis is unclear. AIMS: This study aims to estimate the recurrence rate in young cryptogenic stroke patients with and without patent foramen ovale. PATIENTS AND METHODS: One hundred eighty-six cryptogenic stroke patients (aged 18-45 years) were prospectively followed for up to five-years. They were divided into two groups according to the echocardiographic presence of patent foramen ovale. All patients received aspirin (100 mg/day) for secondary prevention. RESULTS: Mean age was 32·3 (standard deviation 7·9) years. During the mean follow-up of 66 months five patients with patent foramen ovale had recurrent strokes compared with 11 patients without patent foramen ovale. The average annual rate of recurrent cerebral ischemia was 1·1% and 1·6% for patients with and without patent foramen ovale, respectively. The recurrence rate did not increase with the presence of patent foramen ovale, atrial septal aneurysm or other variables. More than 60% of the reported cases achieved a good functional outcome. CONCLUSIONS: Young patients with cryptogenic ischemic stroke with and without patent foramen ovale have a low recurrence rate in a long-term follow-up and most present a favorable outcome. Patent foramen ovale with or without atrial septal aneurysm did not increase the risk of recurrence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".