Development of an Individualized Yoga Intervention to Address Fatigue in Hospitalized Children Undergoing Intensive Chemotherapy
Bibliographic record
Abstract
Purpose Fatigue is an important problem in children receiving intensive chemotherapy and hematopoietic stem cell transplantation (HSCT). Exercise may be an effective intervention for fatigue. Individualized yoga represents an ideal intervention because it can be tailored according to an individual child's needs. Little is known about how to structure a standardized yoga program for intensivelytreated children. Therefore, this study describes the development of a yoga program and an approach to monitoring sessions suitable for hospitalized children receiving intensive chemotherapy or HSCT. Methods The yoga program was designed to increase mobility in hospitalized children and to provide children with relaxation techniques that could be used independently in a variety of environments. The program was founded on 4 key tenets: safety, adaptability, environmental flexibility, and appeal to children. We also developed quality and consistency assurance procedures. Results A menu format with a fixed structure was selected for the yoga program. Each yoga session contained up to 6 sections: breathing exercises, warmup exercises, yoga poses, balancing poses, cool-down poses, and final relaxation. Yoga instructors selected specific yoga poses for each session from a predetermined list organized by intensity level (low, moderate, or high). Monitoring procedures were developed using videotaping and multirater adjudication. Conclusion We created a standardized yoga program and an approach to monitoring that are now ready for incorporation in clinical trials. Future work should include the adaptation of the program to different pediatric populations and clinical settings.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".