Stroke prevention by percutaneous closure of patent foramen ovale: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: The role of percutaneous closure of patent foramen oval (PFO) in patients with cryptogenic stroke has been very controversial for years due to a lack of clear evidence. OBJECTIVE: Systematic review and meta-analysis of the effect of percutaneous PFO closure for secondary prevention of cryptogenic strokes as compared to best medical therapy (BMT). DATA SOURCES: Trials were identified through a literature search until 28 May 2013. STUDY SELECTION: Controlled clinical trials (randomised and non-randomised) comparing percutaneous PFO closure with BMT. DATA EXTRACTION AND SYNTHESIS: Main end point of interest was stroke. A random effects model was used to calculate the pooled relative risks (RR) with 95% CIs. RESULTS: A total of 14 studies (three randomised controlled trials (RCT) and 11 non-randomised observational studies (non-RCT)), and a total of 4335 patients were included for this analysis. There was no significant treatment effect of PFO closure regarding stroke among the RCT (RR 0.66, 95% CI 0.37 to 1.19, p=0.171). However, among non-RCT stroke was reduced (RR 0.37, 95% CI 0.20 to 0.67, p<0.001) after PFO closure. A time-to-event (stroke) analysis, combining all three RCT and the two non-RCT which applied strict multivariate adjustments, showed a borderline significant risk reduction after PFO closure (HR 0.58, 95% CI 0.33 to 0.99, p=0.047). Neither risk of bleeding nor mortality differed significantly between the groups. However, there was a higher incidence of new onset atrial fibrillation in the closure group (RR 3.50, 95% CI 1.47 to 8.35, p=0.005). CONCLUSIONS: Percutaneous closure of PFO in patients with cryptogenic stroke does not appear superior to medical therapy according to currently available randomised data. Furthermore, it is associated with an increased incidence of atrial fibrillation. However, there are signals pointing towards a potential benefit and more research should be strongly encouraged.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".