Exercise Intolerance and the Impact of Physical Activity in Children Treated With Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Hematopoietic stem-cell transplant (SCT) is increasingly used to treat children with cancer, and survival following SCT is improving. One predominant consequence of childhood cancer therapy is increased physical morbidity, which is worse in pediatric SCT recipients compared with children treated with chemotherapy or radiation alone. There are many factors that contribute to exercise intolerance and reduced physical function during the pretransplant, peritransplant, and posttransplant phases. These include side effects from chemotherapy or radiation, excessive immobility due to bed rest, infections, the negative effects of immunosuppressants, and graft vs host disease, all of which can impair cardiorespiratory fitness, muscle strength, and muscle function. Few studies have investigated the effects of exercise in childhood SCT recipients. In a small number of published studies, exercise interventions have been demonstrated to improve cardiorespiratory fitness, preserve or increase muscle mass, and improve muscle strength in children following SCT. The use of exercise as medicine may be a noninvasive and nonpharmaceutical treatment to target physical complications post-SCT. Researchers and health-care professionals should work together to develop exercise prescription guidelines for this unique and important population.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".