Meeting Report: Consensus Recommendations for a Research Agenda in Exercise in Solid Organ Transplantation
Bibliographic record
Abstract
With improved survival rates in solid organ transplantation there has been an increased focus on long-term outcomes following transplant, including physical function, health-related quality-of-life and cardiovascular mortality. Exercise training has the potential to affect these outcomes, however, research on the optimal timing, type, dose of exercise, mode of delivery and relevant outcomes is limited. This article provides a summary of a 2-day meeting held in April 2013 (Toronto, Canada) in which a multi-disciplinary group of clinicians, researchers, administrators and patient representatives engaged in knowledge exchange and discussion of key issues in exercise in solid organ transplant (SOT). The outcomes from the meeting were the development of top research priorities and a research agenda for exercise in SOT, which included the need for larger scale, multi-center intervention studies, development of standardized outcomes for physical function and surrogate measures for clinical trials, examining novel modes of exercise delivery and novel outcomes from exercise training studies such as immunity, infection, cognition and economic outcomes. The development and dissemination of "expert consensus guidelines," synthesizing both the best available evidence and expert opinion was prioritized as a key step toward improving program delivery.
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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 | 0.221 | 0.277 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.014 | 0.017 |
| Research integrity | 0.052 | 0.040 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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".