Abstract 11058: Pooled Analysis of Six Randomized Trials Comparing Device Closure of Patent Foramen Ovale After Stroke to Medical Treatment: Overall Effects and Patient Selection Using a Causal Classification System
Bibliographic record
Abstract
Introduction: Patent foramen ovale (PFO)-associated strokes account for ~10% of ischemic strokes in adults aged 18-60. Despite overall beneficial effects of device closure on stroke recurrence risk, the best treatment option for any individual patient is often unclear. Methods: We pooled individual participant-level data from all 6 RCTs comparing PFO closure with percutaneous devices plus medical therapy vs medical therapy alone. The primary outcome was recurrent ischemic stroke. We used Cox proportional hazards models to estimate adjusted HRs, with study-specific baseline hazards and a fixed treatment effect. Pre-specified primary subgroup analyses used the Risk of Paradoxical Embolism (RoPE) Score (a 10 point score with higher scores reflecting younger age and absence of vascular risk factors) and the PFO-Associated Stroke Causal Likelihood (PASCAL) algorithm, a classification system that combines the RoPE Score with presence/absence of high risk PFO features (either atrial septal aneurysm or large-sized shunt) to group patients into 3 causal relatedness categories: Unlikely (RoPE< 7 and no high risk PFO feature); Possible (either RoPE > 7 or any high risk PFO feature); or Probable (both RoPE > 7 and any high risk PFO feature). The protocol was registered at PROSPERO. Results: A total of 121 recurrent strokes occurred in 3740 patients over median follow up of 57 months (IQR 24-64). The annualized incidence of stroke was lower with device vs medical therapy: 0.47% (0.34-0.65) vs 1.09% (0.87-1.36); adjusted HR 0.41 (0.27-0.60), p < 0.001. The main results were robust to alternative analytic approaches. The primary subgroup analyses (Figure) showed statistically significant and clinically important interaction effects. Discussion: PFO closure reduces recurrent ischemic stroke in a robust manner. However, patients with neither a RoPE Score > 7 or a high risk PFO feature (i.e. PASCAL Unlikely) do not appear to benefit, even in the well-selected trial population.
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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 | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".