What is good for the circulation also lessens cancer risk
Bibliographic record
Abstract
Although there have been substantial advances in the prevention and management of cardiovascular disease (CVD) and its complications, a new player and concept has entered the scene, namely an association between CVD and cancer. There were two alerting signals to this remarkable coupling. On the one hand, disconcerting evidence has been provided that suggests that the use of angiotensin receptor blockers could be increasing the development of cancers.1–3 On the other hand, evidence has been provided that the preventative effects of aspirin both as an antithrombotic agent in CVD prevention and the subsequent discovery of its effects in lessening the development of cancer, especially but not only primary gastrointestinal and distant metastases.4 In addition, ideal cardiovascular health has been shown to be inversely associated with incident cancer in the Atherosclerosis Risk In Communities (ARIC) study.5 The American Heart Association (AHA) has now widened its health goals to adherence to seven ideal heart health metrics that are aimed at lessening the incidence of both CVD and cancer as part of its 2020 goals.6 To achieve this goal, the AHA is therefore pursuing partnerships with cancer advocacy groups to achieve reductions in chronic disease prevalence. The ideal health factors are four ideal self-help health metrics and three ideal measured health metrics (an untreated total cholesterol <200 mg/dL, untreated blood pressure <120 mmHg systolic and 80 mm Hg diastolic, and untreated fasting serum glucose <100 mg/dL) ( Table 1 ). Ambitious plans to reduce both CVD and cancer will be communicated to the American public through the ‘Life's Simple Seven campaign’. Fundamental to the current ‘war on cancer’ is the role of lifestyle measures …
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".