The crosstalk between Stroke and Cancer: Incidence of cancer after a first-ever cerebrovascular event in a population-based study
Bibliographic record
Abstract
Abstract Objectives: To determine the cancer incidence after the first-ever cerebrovascular event (CVE) and compare it to the cancer incidence in the population from the same region. Methods: We evaluated 1069 patients with a first-ever CVE (Ischaemic or haemorrhagic stroke and Transient Ischaemic Attack) from a prospective population registry of stroke and transient focal neurological attacks, diagnosed between 2009 and 2011. We conducted a structured search to identify cancer-related variables and case-fatality for a period of 8 years following CVE. Cancer incidence in CVE patients was compared to the North Region Cancer Registry (RORENO). Results: We found that 90/1069 (8.4%) CVE patients developed cancer after a first-ever CVE. Overall cancer annual incidence rate was higher after a CVE (820/100,000, 95%CI: 619–1020) than in general population (513/100,000, 95%CI: 508–518). In the 45–54 age group cancer incidence post-CVE was 3.2-fold (RR, 95%CI: 1.6–6.4) higher compared to the general population, decreasing gradually in older age-groups. Median time between CVE and cancer was 3.2 years (IQR = 1.4–5.2). Lower respiratory tract and colorectal were the most frequent cancer types. In univariable models, male sex (sHR = 1.78, 95%CI: 1.17–2.72, p = 0.007), tobacco use (sHR = 2.04, 95%CI: 1.31–3.18, p = 0.002) and peripheral artery disease (sHR = 2.37, 95%CI: 1.10–5.13, p = 0.028) were associated to higher cancer risk after CVE. After adjustment, tobacco use (sHR = 1.84, 95%CI: 1.08–3.14, p = 0.026) remained associated to a higher risk of cancer. Conclusions: At the population level, patients presenting a first-ever CVE have higher cancer incidence, that is particularly prominent in younger age-groups. Higher cancer incidence, delayed cancer diagnosis and increased mortality post-CVE warrants further research on long-term cancer surveillance in first-ever CVE survivors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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".