An 8-Week Virtual Exercise Training Program for Pediatric Solid Organ Transplant Recipients
Bibliographic record
Abstract
PURPOSE: Musculoskeletal strength can be impaired in pediatric solid organ transplant recipients. Exercise training programs can be beneficial but in-person delivery can be challenging; virtual exercise programs can alleviate some of these challenges. This feasibility study aimed to deliver an 8-week virtual exercise program in pediatric solid organ transplant recipients. METHOD: Program delivery occurred 3 times per week for 30 minutes. An exercise stress test was completed prior to program start. The Bruininks-Oseretsky Test of Motor Proficiency strength subtest and self-report surveys were used to assess musculoskeletal strength, quality of life, fatigue, and physical activity. Contact was maintained through a text messaging platform. Z scores were calculated using standardized normative data. Medians (interquartile range) are reported for all other data. RESULTS: Eleven participants completed the program (2 liver, 5 kidney, 4 heart; 58% females; median age = 11.5 [10.3-13.8] y). Six participants attended ≥60% of classes, 5 participants attended <50% of classes. After 8 weeks, strength scores improved (Z score, Pre: -1.0 [-1.65 to -0.60] to Post: -0.2 [-1.30 to 0.40]; P = .007) with no change in other outcome measures. CONCLUSION: The virtual exercise program was delivered without technical issues and received positive participant feedback. Engagement and costs need to be considered.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".