Temporal trends in cardiovascular mortality among patients with hematological malignancies: a 20-year perspective
Bibliographic record
Abstract
BACKGROUND: We present an analysis of cardiovascular-related deaths specific to hematological cancer patients in the United States from 1999 to 2020, examining trends in relation to age, gender, and type of hematological cancer. RESEARCH DESIGN AND METHODS: Utilizing the Multiple Cause of Death databases, our research included 88,146 decedents with cardiovascular primary cause of death and with hematologic disease. We determined the percentage of cardiovascular deaths associated with each disease category. Furthermore, we developed age-adjusted mortality rates, categorizing them based on sex, age, race, Latino origin, and the type of hematological cancer. RESULTS: Between 1999 and 2020, there was a decreasing temporal trend in overall cardiovascular mortality for lymphoma, leukemia and multiple myeloma (-38.8% -31.8% & -29.4%). The most common cardiovascular mortality cause in the hematological malignancy population was ischemic heart disease, followed by cerebrovascular disease (53.4%, 20.2%). African American, Asian, and White patients showed decreasing for overall CV death for all hematological malignancies, with African American subgroups showing the lowest mortality reduction over time (AAMR: -26.8%, -41.2%, -33.3%). However, hypertension mortality increased for most racial groups. CONCLUSIONS: Over the last 2 decades, the rate of cardiovascular mortality amongst patients with underlying hematological malignancy has decreased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".