An interdisciplinary approach to ensure adherence to exercise and potentially reduce adverse cardiac events in cancer patients: primers in cardio-oncology
Bibliographic record
Abstract
Exercise training can improve mortality, quality of life, exercise capacity and morbidity in cancer patients. Although current cardio-oncological guidelines recommend exercise intervention in cancer patients, a structured, interdisciplinary approach from diagnosis to maintenance of therapy including repetitive sports cardiological assessment to guide exercise intervention has not been established. Currently, exercise prescriptions are based on assessment of peak oxygen consumption, which does not differentiate between central (stroke volume and heart rate reserve) and peripheral (peripheral oxygen difference) limitations. Knowledge of these mechanisms could facilitate more effective exercise prescriptions because cancer subtypes may respond differently to exercise stimuli requiring individualized and cancer-specific exercise intervention. Our approach uses simultaneous cardiopulmonary exercise testing and stress echocardiography to analyze the entire oxygen cascade in one exam. Based on these findings, we propose individualized assessment of treatment and cardiovascular risk as well as exercise prescriptions. As cardiopulmonary limitations indicative of cancer therapy-related cardiac dysfunction or cardiotoxicity may first be detected during exercise, our approach may help to address cancer therapy-related adverse events earlier. Our approach contains repetitive exercise testing which should serve to re-assess efficacy of exercise intervention. Results of each sports cardio-oncological assessment is fed back to the treating oncologists as it may provide valuable insights to adapt treatment regimens. In addition, we propose a transition from supervised on-site and home-based to self-directed training, which may achieve better long-term training adherence from prehabilitation to post-treatment. In the advent of precision medicine and oncology we provide a concept for precision sports cardio-oncological care to tailor individual exercise prescriptions based on pathophysiological findings during exercise testing.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".