Risk of Cerebrovascular Events in 178 962 Five-Year Survivors of Cancer Diagnosed at 15 to 39 Years of Age
Bibliographic record
Abstract
BACKGROUND: Survivors of teenage and young adult cancer are at risk of cerebrovascular events, but the magnitude of and extent to which this risk varies by cancer type, decade of diagnosis, age at diagnosis, and attained age remains uncertain. This is the largest-ever cohort study to evaluate the risks of hospitalization for a cerebrovascular event among long-term survivors of teenage and young adult cancer. METHODS: The population-based TYACSS (Teenage and Young Adult Cancer Survivor Study) (N=178,962) was linked to Hospital Episode Statistics data for England to investigate the risks of hospitalization for a cerebrovascular event among 5-year survivors of cancer diagnosed when 15 to 39 years of age. Observed numbers of first hospitalizations for cerebrovascular events were compared with that expected from the general population using standardized hospitalization ratios (SHRs) and absolute excess risks per 10 000 person-years. Cumulative incidence was calculated with death considered a competing risk. RESULTS: Overall, 2782 cancer survivors were hospitalized for a cerebrovascular event-40% higher than expected (SHR=1.4, 95% confidence interval, 1.3-1.4). Survivors of central nervous system (CNS) tumors (SHR=4.6, 95% confidence interval, 4.3-5.0), head and neck tumors (SHR=2.6, 95% confidence interval, 2.2-3.1), and leukemia (SHR=2.5, 95% confidence interval, 1.9-3.1) were at greatest risk. Males had significantly higher absolute excess risks than females (absolute excess risks =7 versus 3), especially among head and neck tumor survivors (absolute excess risks =30 versus 11). By 60 years of age, 9%, 6%, and 5% of CNS tumor, head and neck tumor, and leukemia survivors, respectively, had been hospitalized for a cerebrovascular event. Beyond 60 years of age, every year, 0.4% of CNS tumor survivors were hospitalized for a cerebral infarction (versus 0.1% expected), whereas at any age, every year, 0.2% of head and neck tumor survivors were hospitalized for a cerebral infarction (versus 0.06% expected). CONCLUSIONS: Survivors of a CNS tumor, head and neck tumor, and leukemia are particularly at risk of hospitalization for a cerebrovascular event. The excess risk of cerebral infarction among CNS tumor survivors increases with attained age. For head and neck tumor survivors, this excess risk remains high across all ages. These groups of survivors, particularly males, should be considered for surveillance of cerebrovascular risk factors and potential pharmacological interventions for cerebral infarction prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".