P.073 The Effect of Cancer on The Prevalence Of Stroke Survivorship In Canada – A Cross-Sectional Study
Bibliographic record
Abstract
Background: In Canada, it’s unknown if the prevalence of stroke survivorship differs in the population with active cancer compared to those without cancer. Methods: We analyzed the 2015-2016 iteration of the Canadian Community Health Survey. The prevalence of stroke survivorship was compared across risk factors using descriptive statistics. A multivariable logistic regression model was used to assess the association between cancer and prevalence of stroke survivorship. Covariates were assessed for effect modification and confounding using the maximum likelihood estimation method. Results: We analyzed 89,285 subjects. The prevalence of cancer and the prevalence of suffering from the effects of a stroke were 2.09% and 1.56%, respectively. Cancer was significantly associated with an increased prevalence of stroke survivorship with an odds ratio (OR) of 1.56 (95%CI: 1.24 – 1.98) after adjusting for age, sex, smoking status, education, household income, dyslipidemia, hypertension, diabetes. The association was stronger in younger age groups: the youngest age group (18 – 49 years) had the highest OR (6.49, 95%CI:2.01 – 20.94) for suffering from the effects of a stroke in association with the presence of cancer. Conclusions: In Canada, the presence of active cancer increases the odds of suffering from the effects of a stroke, particularly in the youngest age group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".