A Population-Based Study of Sex Differences in Cardiovascular Disease Mortality Among Adults with Ocular Cancer in the United States, 2000–2021
Bibliographic record
Abstract
Little is known about the manifestation of cardiovascular diseases (CVD) among individuals with ocular cancer (OC), a population for whom reports on sex-based differences in survival remain inconsistent. We evaluated the occurrence of CVD mortality after the diagnosis of OC in the United States. We used data from 11,460 adults diagnosed with OC from 2000 to 2021 who were ≥18 years and were enrolled in the Surveillance, Epidemiology, and End Results program. We used competing risk models to estimate hazard ratios (HR) and 95% confidence intervals (CI). About 55% of adults were male, with uveal melanoma being the most common OC (72.1%). During a median follow-up of 5.4 years, 4561 deaths occurred, with 15% attributable to CVD. In models adjusted for sociodemographic and clinico-pathophysiological factors, male adults had elevated risk for CVD mortality (HR: 1.54, 95%CI: 1.31-1.81). The sex difference in CVD mortality was more prominent for adults diagnosed with OC before 65 years of age (HR: 2.15; 95%CI: 1.48-3.11). These associations remained largely unchanged in propensity score analysis. In this study of adults with OC, CVD deaths were higher among young and middle-aged males. Implementation of optimal cardiovascular health interventions after diagnosis of OC, especially among men, holds promise in enhancing survival in this population.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".