Abstract P608: Risk of Cancer Following an Ischemic Stroke: A Retrospective Cohort Study From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Background: Cancer promotes thromboembolism through inflammation and hypercoagulability, and an ischemic stroke may be the first sign of an occult malignancy. Stroke survivors may be at higher risk of incident cancer diagnosis, 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 (CLSA) is a large, national population-based cohort of Canadian women and men aged 45-85 years when recruited between 2011 and 2015. Participants provided information on socio-demographics and prior diagnoses. We used data from the comprehensive sub-group (n=30,097) to build a retrospective cohort of participants with and without prior stroke by exact matching for age (1:4 ratio). We used log-binomial regression models to estimate the risk of cancer in people with versus without ischemic stroke while adjusting for shared risk factors. Results: We included 920 individuals in the stroke group and 3,680 individuals in the control group, respectively followed for a median of 10 (interquartile range [IQR]: 4, 17) and 11 years (IQR: 5, 19). Most inclusion events in the stroke group were minor strokes or transient ischemic attacks (n=614, 66.7%). We observed a higher incidence of cancer in the first year after stroke that declined thereafter (p=0.030). The risk of new cancer diagnosis after stroke was significantly increased (relative risk: 2.38; 95% confidence interval: 1.18, 4.63; p-value=0.012) 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 risk of new cancer diagnosis in the first year after an ischemic stroke is about 2.4 times higher as compared to age-matched individuals from the general population without stroke after adjustments. Surveillance bias may explain a portion of post-stroke cancer diagnoses in the stroke group although a CLSA tendency to recruit healthier participants likely led to an underestimation of post-stroke cancer risk. Prospective experimental trials are needed to quantify 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".