Abstract TP306: Prevalence of right-to-left shunting on transthoracic echocardiography in patients with cancer and stroke
Bibliographic record
Abstract
Background: Cancer is a leading cause of mortality and a well-known risk factor for ischemic stroke. However, the relationship between cancer and stroke is not well studied. Previous research in this area suggests presence of right-to-left shunt as a possible underlying mechanism of paradoxical embolism in patients with cancer diagnosis within one year of the stroke. Thus, our study seeks to further investigate the potential role of right-to-left shunting in stroke occurrence among cancer patients. Methods: This is a retrospective cohort study with our population consisting of patients presenting to the Ottawa Hospital with ischemic stroke between January 01, 2020, and December 31, 2022, who have undergone transthoracic echocardiography. Presence of right-to-left shunting is identified on echocardiography in patients without cancer and those with cancer diagnosis one year before and one year after the ischemic stroke. The prevalence of shunt is assessed using 95% confidence intervals (CI). Results: Among 495 patients (37% female, median age 53 years) presenting with ischemic stroke, 47 (9.5%) had cancer diagnosis within one year of stroke, with 12 patients (25.5%, 95% CI 14 - 40) diagnosed with a shunt. In contrast, among 448 patients (90.5%) that did not have a cancer diagnosis within one year of their stroke, 133 patients (30%, 95% CI 25 - 34) were identified to have a shunt. Conclusion: The prevalence of right-to-left shunting tends to be lower in patients presenting with ischemic stroke and active cancer diagnosis. This result is consistent with a recent study in this area indicating a higher rate of shunt among patients without cancer than those with cancer. Our finding does not support the hypothesis that cancer-associated stroke is related to right-to-left shunting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".