Abstract 198: PFO and Recurrent Stroke: Predictors Differ In Patients With “Probable Pathogenic” Versus Other PFOs
Bibliographic record
Abstract
Background The “RoPE Score” is a predictive model created to stratify patients by the likelihood that a patent foramen ovale (PFO) is incidental or pathogenic using clinical variables. We hypothesized that the predictors of recurrent stroke differ between patients with pathogenic and incidental PFOs. Methods Patients in the Risk of Paradoxical Embolism (RoPE) database with cryptogenic stroke (CS) and PFO were classified as having a probable pathogenic PFO (RoPE Score of >6, estimated PFO attributable fraction 72-99%, n=646) and others (RoPE Score of <6 points estimated PFO attributable fraction 0-72%, n=678). We tested 15 clinical, 5 radiological, and 3 echo variables for associations with stroke recurrence using Cox survival models with component database as a stratification factor. An interaction with RoPE score was checked for the variables that were significant. Results Follow-up was available for 91%, 80%, and 58% at 1, 2, and 3 years. Overall, a higher recurrence risk was associated with an index TIA, not being on a statin at baseline, and having a prior radiological stroke. For the low RoPE score group, older age, male sex, high cholesterol and antiplatelet (vs warfarin) treatment predicted recurrence. For those with high RoPE scores, predictors were prior (clinical) stroke/TIA and 2 echo features: septal hypermobility and a small shunt (<10 bubbles). Conclusions Predictors of recurrence differ when PFO relatedness is classified by the RoPE Score. The hypothesis that patients with CS and PFO form a heterogenous group with different stroke mechanisms is supported. Conventional stroke risk factors were strong predictors among patients with lower RoPE scores. Echocardiographic features - including a counterintuitive association between smaller shunts and increased recurrence risk - were uniquely predictive in the high RoPE score group (likely pathogenic PFO).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".