Abstract 33: PFO in Cryptogenic Stroke: An Analysis of “High Risk” Features on Transesophageal Echocardiography in the Risk of Paradoxical Embolism Database
Bibliographic record
Abstract
Introduction: A patent foramen ovale (PFO) discovered in the setting of a cryptogenic stroke (CS) may be stroke-related or incidental. We have developed a score to stratify CS patients according to the probability that the event is attributable to a PFO (the Risk of Paradoxical Embolism (RoPE) Score), based on easily obtainable clinical and neuroradiological (but not transesophageal echocardiographic (TEE) variables. In this study, we examined whether putative “high risk” TEE features - shunt size, presence of a hypermobile septum, and presence of a right-to-left shunt at rest - vary across RoPE Score strata. Methods and Results: The RoPE Study combined existing cohort studies to create a pooled database of patients with CS and PFO. We dichotomized patients into groups and examined whether putative high risk TEE features are seen more frequently in those with “probable stroke-related” PFOs (RoPE Score of >6, estimated PFO attributable fraction 72-99%, n=637) than in those with lower RoPE Scores (<6, PFO attributable fraction 0-71%, n=657). None of the TEE features differed between the groups (large physiologic size, p=0.53; hypermobile septum, p=0.44; shunt at rest, p=0.11). Extensive additional exploratory analyses did not reveal any consistent associations between different RoPE Score strata and presumptive high risk echocardiographic features. Conclusion: We found no evidence that proposed TEE markers for “high risk” PFOs correlate with the estimated likelihood that a PFO is related to an index CS. Additional imaging tools or better standardization of imaging techniques are needed to determine whether specific anatomic features are associated with whether a discovered PFO is likely to be related to CS.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".