Stroke Recurrence and its Prevention in Patients with Patent Foramen Ovale
Bibliographic record
Abstract
BACKGROUND: It is unclear whether medical or invasive (surgical or catheter interventional) treatment is preferable to prevent recurrence of cerebral ischemia in patients with patent foramen ovale (PFO) as the suspected cause of stroke and what the role of concomitant risk factors is in stroke recurrence. METHODS: Over a period of ten years, 124 patients (mean age 51 +/- 15 years) with cryptogenic cerebral ischemia and PFO were included into the study and prospectively followed over a mean of 52 +/- 32 months. Of these, 83 were treated medically, 34 underwent transcatheter closure, and seven had surgical closure of the foramen. Of the medically treated patients, 11 stopped medication during follow-up. Recurrent ischemic events and risk factors for recurrence were analyzed. RESULTS: Annual stroke recurrence rates were generally low and comparable in catheter and medically treated patients, and in patients who had stopped medication (2.9%/2.1%2.2%/year). Patients suffering from recurrence after transcatheter closure (n = 2) both had residual shunts. No stroke recurrence was observed in the few surgically treated patients. An atrial septal aneurysm was not a predictor of recurrent or multiple strokes (p > 0.05, OR = 0.31, and OR = 0.74). Large shunts and a history of previous ischemic events were considerably more frequent in patients with recurrent strokes (p < 0.05, OR = 5.0, and OR = 4.4). Pulmonary embolism and case fatality rates were significantly higher in patients with stroke recurrence (p < 0.001, and p < 0.01). CONCLUSIONS: The absolute risk of recurrent cerebrovascular events in patients with PFO receiving medical or catheter interventional therapy is low. The small group of untreated patients had a comparably low rate of stroke recurrences. Previous ischemic events and shunt size were risk factors in this observational study. Given conflicting findings across multiple studies, enrollment into a randomized controlled trial would be the optimal choice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".