Abstract TP216: Ischemic Stroke In Patients With Cancer Compared To Ischemic Stroke In Patients Without Cancer - A Cohort Study Using Synthetic Data
Bibliographic record
Abstract
Background: Patients with cancer are at an increased risk for ischemic stroke (IS) compared to those without cancer. The objectives of this study are 1) to examine risk factors for stroke in cancer and non-cancer patients, and 2) to identify predictive factors for recurrent IS in cancer patients. Methods: We performed a retrospective cohort study using MDClone, a platform that produces synthetic datasets based on real health system data from the Ottawa Hospital Data Warehouse. We analyzed all subjects with a diagnosis of cancer (excluding non-melanoma skin cancer or primary central nervous system malignancies) and IS within a 2-year period preceding and following their cancer diagnosis, and all IS patients without cancer, from the same time period (2000-2019). Patients were followed until May 2019. A forward selection, stepwise multivariate logistic regression model was used to assess the association between recurrent IS (primary outcome) and baseline characteristics. A sensitivity analysis was performed with only survivors in the cancer cohort. Results: We analyzed 10,875 subjects with IS: 1,250 had cancer and 9,625 did not. In cancer subjects, there was a higher prevalence of chronic obstructive pulmonary disease (8.4% vs 4.7%), previous IS (1.9% vs 0.1%), and previous venous thromboembolism (VTE) (8.3% vs 1.5%); the prevalence of atrial fibrillation and vascular risk factors was similar between the two groups. Recurrent IS occurred in 11.0% of cancer subjects and 12.1% of non-cancer subjects. In cancer subjects, the only significant predictor of recurrent IS was previous IS (OR 3.8, 95%CI 2.6 - 5.6). A sensitivity analysis of survivors amongst cancer subjects revealed an even stronger relationship between previous IS and recurrent IS (OR 4.4 95%CI 2.7 - 7.0). In the non-cancer subjects, significant predictors for recurrent IS include older age, while hypertension, dyslipidemia, and history of hemorrhagic stroke were negative predictors of recurrent IS. Conclusion: We found cancer patients with IS have a higher prevalence of COPD, previous IS and VTE, and that previous IS is an important predictor of recurrent IS in cancer patients. These results highlight the importance of identifying optimal secondary prevention treatments in cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".