An 8-step approach for the systematic development of an evidence-based exercise program for patients undergoing hematopoietic stem cell transplantation
Bibliographic record
Abstract
Background: A tailored and reliable intervention program developed based on evidence is necessary for patients with serious health conditions. Objective: We describe the development of an exercise program for HSCT patients based on evidence from a systematic process. Methods: We developed the exercise program for HSCT patients using eight systematic steps: (1) a literature review, (2) understanding patient characteristics, (3) first expert group discussion, (4) development of the first draft of the exercise program, (5) a pre-test, (6) second expert group discussion, (7) a pilot randomized controlled trial (n=21), and (8) a focus group interview. Results: smartphone, and prior education sessions. In the pilot trial, the adherence to the exercise program was only 44.7%, however, some changes in physical functioning and body composition favored the exercise group despite the small sample size. Conclusion: Strategies to improve adherence to this exercise program and larger sample sizes are needed to adequately test if the developed exercise program may help patients improve physical and hematologic recovery after HSCT. This study may help researchers develop a safe and effective evidence-based exercise program for their intervention studies. Moreover, the developed program may benefit the physical and hematological recovery in patients undergoing HSCT in larger trials, if exercise adherence is improved. Clinical trial registration: https://cris.nih.go.kr/cris/search/detailSearch.do?seq=24233&search_page=L, identifier KCT 0008269.
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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 | 0.200 | 0.223 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".