Challenges of Implementing Multicenter Studies of Yoga for Pediatric Cancer and Hematopoietic Stem Cell Transplantation Recipients
Bibliographic record
Abstract
The primary objective of this work was to determine the feasibility of a randomized trial of individualized yoga for children receiving intensive chemotherapy and for hematopoietic stem cell transplantation (HSCT) recipients outside of the principal coordinating institution. We evaluated the feasibility of a randomized trial of individualized yoga versus an iPad control program at a site where external yoga instructors were hired and compensated per session. Subjects were children receiving intensive chemotherapy for hematological malignancies and autologous or allogeneic HSCT recipients expected to be hospitalized for 3 weeks. Yoga or iPad control contact occurred daily for 21 days (excluding weekends and holidays); fatigue and quality-of-life outcomes were measured at baseline, day 10, and day 21. Ten eligible subjects were identified; six subjects consented and were enrolled. Three were randomized to the individualized yoga intervention and three to the iPad control program. The median age of participants was 12 (range 8-15) years, and 2 (33%) were boys. Challenges primarily related to the hiring of yoga instructors who were not trained in research methods. We found issues with: (1) logistics of hiring, training, and retaining instructors; (2) communication between teams; (3) fidelity to the protocol and outcome assessments; and (4) ensuring safety. We found that a randomized trial of individualized yoga presented new challenges when relying on externally contracted yoga instructors. Future multicenter studies of yoga should seek to better integrate practitioners within the research team to improve processes, communication, fidelity to the protocol, and safety.
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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 | 0.464 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".