Cardiovascular Diseases, Vital Organ Fibrosis, and Chronic Inflammation Associated with High-Intensity and/or High-Volume Exercise Training: Double-Edged Sword Effects of Vigorous Physical Activity in Elderly People and/or in Middle-Age Cancer-Therapy-Treated Patients
Bibliographic record
Abstract
Cardiotoxicity, cardiovascular diseases (CVDs), hypertension, hepatotoxicity, and respiratory problems occurring several months to several years post-chemotherapy and/or radiotherapy are increasingly documented by scientists and clinicians. Anthracyclines, for example, were discovered in the late 1960s to be dose-dependently linked to induced cardiotoxicity, which frequently resulted in cardiomyopathy and heart failure. Most of those changes have also been associated with aging. While it is well known that exercise can slow down cellular aging processes, lessen the effects of chemotherapy, improve the effectiveness of cancer treatments, and prevent health problems in the general population, it remains unclear how exercise volume or intensity may affect the overall benefits of physical activity on health. For instance, higher rates of sudden cardiac arrest or coronary artery calcification have been found in marathon and ultra-marathon runners. Several additional pathological consequences have also been reported recently on many organs of those athletes. This review reports the most recent evidence suggesting that excessive intensity and/or volume may have deleterious effects on health. These findings are in clear contrast with the popular belief that all forms of physical activity can generally reduce the pathological changes associated with aging or cancer therapies. In conclusion, high-intensity training (HIT) and/or high-volume training (HVT) should not be recommended for middle-age and elderly people who have had cancer therapies or not in order to avoid an exacerbation of the consequences of aging or long-term cancer treatment effects on vital organ structures and functions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".