Risk of Cancer Following an Ischemic Stroke in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Stroke survivors may be at higher risk of incident cancer, although the magnitude and the period at risk remain unclear. We conducted a retrospective cohort study to compare the risk of cancer in stroke survivors to that of the general population. METHODS: The Canadian Longitudinal Study on Aging is a large population-based cohort of individuals aged 45-85 years when recruited (2011-2015). We used data from the comprehensive subgroup (n = 30,097) to build a retrospective cohort with individual exact matching for age (1:4 ratio). We used Cox proportional hazards models to estimate hazard ratios of new cancer diagnosis with and without a prior stroke. RESULTS: We respectively included 920 and 3,680 individuals in the stroke and non-stroke groups. We observed a higher incidence of cancer in the first year after stroke that declined afterward (p-value = 0.030). The hazard of new cancer diagnosis after stroke was significantly increased (hazard ratio: 2.36; 95% CI: 1.21, 4.61; p-value = 0.012) as compared to age-matched non-stroke participants after adjustments. The most frequent primary cancers in the first year after stroke were prostate (n = 8, 57.1%) and melanoma (n = 2, 14.3%). CONCLUSIONS: The hazard of new cancer diagnosis in the first year after an ischemic stroke is about 2.4 times higher as compared to age-matched individuals without stroke after adjustments. Surveillance bias may explain a portion of post-stroke cancer diagnoses although a selection bias of healthier participants likely led to an underestimation of post-stroke cancer risk. Prospective studies are needed to confirm the potentially pressing need to screen for post-stroke cancer.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".