Complementary therapies for children undergoing stem cell transplantation
Bibliographic record
Abstract
BACKGROUND: Children undergoing stem cell transplant (SCT) experience high levels of somatic distress and mood disturbance. This trial evaluated the efficacy of complementary therapies (massage, humor therapy, relaxation/imagery) for reducing distress associated with pediatric SCT. METHODS: Across 4 sites, 178 pediatric patients scheduled to undergo SCT were randomized to a child-targeted intervention involving massage and humor therapy, the identical child intervention plus a parent intervention involving massage and relaxation/imagery, or standard care. Randomization was stratified by site, age, and type of transplant. The interventions began at admission and continued through SCT Week +3. Primary outcomes included patient and parent reports of somatic distress and mood disturbance obtained weekly from admission through Week +6 using the Behavioral, Affective, and Somatic Experiences Scales. Secondary outcomes included length of hospitalization, time to engraftment, and usage of narcotic analgesic and antiemetic medications. RESULTS: A mixed model approach was used to assess longitudinal trends of patient and parent report outcomes and to test differences between groups on these measures. Significant changes across time were observed on all patient and parent report outcomes. However, no significant differences between treatment arms were found on the primary outcomes. Similarly, no significant between-group differences were noted on any of the medical variables as secondary outcomes. CONCLUSIONS: Results of this multisite trial failed to document significant benefits of complementary interventions in the pediatric SCT setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".