Skeletal Muscle Dysfunction and Exercise Intolerance in Children Treated with Haematopoietic Stem Cell Transplant—A Pilot Feasibility Study
Bibliographic record
Abstract
Haematopoietic stem cell transplant (HSCT) is an intensive therapy for some pediatric hematological illnesses. Survivors are at risk for adverse effects including exercise intolerance. Peripheral tissue dysfunction may contribute to exercise intolerance; therefore, we examined the feasibility of a magnetic resonance spectroscopy (MRS) protocol to evaluate skeletal muscle metabolism in children post-HSCT. We measured demographic characteristics, aerobic exercise capacity (YMCA protocol), and skeletal muscle function in response to exercise (MRS; Siemens 3T MRI) in five children post-allogeneic HSCT and five age/body mass index-matched healthy controls (HCs). The mean age (± standard deviation) of the HSCT group and HC group were 11 ± 1.2 and 12.8 ± 2.4 years, respectively. Children post-HSCT had a lower peak aerobic exercise capacity compared to HCs (27.8 ± 3.4 vs. 40.3 ± 8.1 mL kg−1 min−1, respectively; p = 0.015). Exercise MRS testing protocols were successfully completed by all HSCT and HC participants; however, MRS-derived skeletal muscle metabolism variables were not different between the two groups. In conclusion, the use of exercise protocols in conjunction with MRS to assess peripheral skeletal muscle metabolism was achievable in children post-HSCT. In the future, larger studies should determine if skeletal muscle function is associated with exercise capacity in children post-HSCT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".