Identifying Outcome Domains for Clinical Trials of Physical Rehabilitation Among Adults Undergoing Solid Organ Transplantation Using a Delphi Approach
Bibliographic record
Abstract
Introduction: A core outcome set (COS) improves the quality of reporting in clinical trials; however, this has not been developed for clinical trials of exercise training among adults undergoing solid organ transplant. Research Question: To explore the perspectives of transplant patients and healthcare professionals on the key outcomes domains that are relevant for clinical trials of exercise in all recipients of transplanted organs. Methods: A Delphi approach was employed with 2 rounds of online questionnaires. Participants rated the importance of outcome domains using a 9-point Likert scale ranging from “not important” to “very important”. A score of 7 to 9 (very important) by 70% or more participants and a score of 1 to 3 (not important) by less than 15% participants were required to keep an outcome domain from the first to the second round. Results: Thirty-six participants completed 2 rounds of questionnaires (90% response rate). After Round 1, 8 outcome domains were considered very important in the pretransplant phase; 16 in the early posttransplant; and 17 in the late posttransplant. Only 1 outcome domain, organ rejection in the early posttransplant phase, met the criteria to be considered very important after Round 2. Conclusion: Although consensus was not reached on the core outcome domains, this study provides preliminary information on which domains are higher priority for patients and professionals. Future work should consider a meeting with key stakeholders to allow for deeper discussion to reach consensus on a core outcome set.
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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 | 0.370 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".