Cardiac Mechanical Performance Assessment at Different Levels of Exercise in Childhood Acute Lymphoblastic Leukemia Survivors
Bibliographic record
Abstract
BACKGROUND: There is a shortage of relevant studies interested in cardiac mechanical performance. Thus, it is clinically relevant to study the impact of cancer treatments on survivors' cardiac mechanical performance to improve our knowledge. The first objective of this study is to assess survivors' cardiac mechanical performance during a cardiopulmonary exercise test (CPET) using both ventricular-arterial coupling (VAC) and cardiac work efficiency (CWE) from cardiac magnetic resonance (CMR) acquisitions. The second objective is to assess the impact of doxorubicin and dexrazoxane (DEX) treatments. METHODS: A total of 63 childhood acute lymphoblastic leukemia survivors underwent a CMR at rest on a 3T magnetic resonance imaging system, followed by a CPET on ergocycle. The CircAdapt model was used to study cardiac mechanical performance. At different levels of exercise, arterial elastance, end-systolic elastance, VAC, and CWE were estimated. RESULTS: We observed significant differences between the different levels of exercise for both VAC ( P <0.0001) and CWE parameters ( P =0.001). No significant differences were reported between prognostic risk groups at rest and during the CPET. Nevertheless, we observed that survivors in the SR group had a VAC value slightly lower than heart rate (HR)+DEX and HR groups throughout the CPET. Moreover, survivors in the SR group had a CWE parameter slightly higher than HR+DEX and HR groups throughout the CPET. CONCLUSIONS: This study reveals that the combination of CPET, CMR acquisitions and CircAdapt model was sensitive enough to observe slight changes in the assessment of VAC and CWE parameters. Our study contributes to improving survivors' follow-up and detection of cardiac problems induced by doxorubicin-related cardiotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".