Abstract B62: The need to improve exercise prescriptions to support care in pediatric oncology
Bibliographic record
Abstract
Abstract Purpose: Cancer survivors’ exposure to chemotherapeutic agents leads to multiple long-term side effects with a decrease in their cardiorespiratory fitness. The first aim was to determine whether cardiorespiratory fitness and physical activity levels were lower among survivors than healthy Canadians, while the second aim was to report associations between genetic variants and cardiorespiratory fitness in survivors. Methods: Cardiorespiratory fitness (VO2peak) and moderate to vigorous physical activity (MVPA) were compared between childhood ALL survivors (N=221) and healthy Canadians (N=825). We performed whole-exome sequencing in survivors. Germline variants (both common and rare) in a selected set of trainability genes were analyzed for an association with cardiorespiratory fitness. Results: Survivors’ VO2 peak was found to be 22% lower than healthy Canadians. The cardiorespiratory fitness level was different between survivors and healthy Canadians despite a clinically equivalent level of MVPA. Positive associations between the cardiorespiratory fitness level and trainability genes (TTN, LEPR, IGFBPI, and ENO3 genes) were reported, especially in female survivors with a low cardiorespiratory fitness level. Conclusion: The cardiorespiratory fitness was significantly lower in survivors, which can be associated with variants in genes related to subjects’ trainability. At this time, it appears that more physical activity would be beneficial to survivors to achieve the same benefits as the healthy population. However, the optimal amount of physical activity needed to reach these benefits is not yet clear. The survivors’ responder or nonresponder status several years after the end of the treatments is unknown. This study has important implications for the field of exercise in oncology. Citation Format: Maxime Caru, Kateryna Petrykey, Mariia Samoilenko, Simon Drouin, Valérie Lemay, Laurence Kern, Lucia Romo, Patrick Beaulieu, Pascal St-Onge, Laurence Bertout, Geneviéve Lefebvre, Gregor Andelfinger, Maja Krajinovic, Caroline Laverdière, Daniel Sinnett, Daniel Curnier. The need to improve exercise prescriptions to support care in pediatric oncology [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B62.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".