Common Risk Factors and Comorbidity of Cardiovascular Diseases and Cancer
Bibliographic record
Abstract
In the first decades of the XXI century, chronic non-communicable diseases (CNDs) retain leadership as the primary cause of disability and complication among people of working age in developed countries of the world. 1 Their total share in adult mortality is about 77%. 2Malignant oncological diseases, along with cardiovascular diseases (CVDs) associated with atherosclerosis, are among the top three causes of mortality in the working-age population.In the United States, among adults, ischemic heart disease is the cause of death in 20.1% of cases, cerebral stroke in 6.3% of cases, and cancer of various localizations in 26.6% of cases.In China, in the structure of mortality rates, cerebral stroke occupies 21.3%, coronary artery disease 18.4%, and cancer of various localizations 31%.In Russia, the contribution of ischemic heart disease to mortality is 34.2%, cerebral stroke 19.9%, and cancer of various localizations 14.6%. 1 According to the forecast of WHO experts, in 2030, CVD will occupy 26.5% of the mortality structure, while oncological diseases of various localization will occupy 8.5%. 3 Meanwhile, the comorbidity of somatic diseases among cancer patients is an urgent problem. 4,5Comorbidity of somatic diseases possesses not only prognostic value for people with cancer, but also negatively affects patients' quality of life.According to the literature, at the time of diagnosis, at least two or three chronic diseases are registered in patients with oncological diseases. 4According to Canadian researchers, in a prospective study with an analysis of more than 600 thousand patients with various localization cancers, more than 5 additional somatic diseases were detected in 23%. 6arious somatic diseases can develop both before the establishment of oncological diseases and after verification of the diagnosis and treatment.Among people with brain cancer before and after the diagnosis of the disease, the greatest number of somatic dis-
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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".