Defining Future Research Priorities in Donation and Organ and Stem Cell Transplantation With Patients, Families, Caregivers, Healthcare Providers and Researchers Within the Canadian National Transplant Research Program
Bibliographic record
Abstract
Background Patients, families, and caregivers have a unique understanding of the diseases they live with and provide care for every day. Their experience and expertise are important and should be taken into consideration when determining research priorities. The aim of this study was to gather the perspectives of Canadian patients, families, caregivers, researchers, and healthcare professionals on what research priorities were important to them in the field of organ and hematopoietic cell transplantation (HCT) and donation within the Canadian National Transplant Research Program (CNTRP). Methods The CNTRP developed a national consultation process, which included a Web-based survey and in-person workshop, to ascertain and validate the viewpoints of the Canadian donation and transplant community. The Web-based survey identified 3 principal research priorities (increasing donation, developing better antirejection drugs and developing tolerance), which were further refined and prioritized during the one-and-a-half day national workshop held in Toronto in November 2015. Results A total of 505 participants answered the Web-based survey, and 46 participants (28 patients, 12 researchers and 6 healthcare professionals) participated in the in-person workshop. Workshop participants ranked the following 2 priorities as the most important in the fields of donation, HCT, and solid organ transplantation: methods for developing a culture of donation (within healthcare organizations and throughout society); and methods for improving graft survival and antirejection therapy. Conclusion The CNTRP will use these results to prioritize future research projects and studies in donation, HCT, and solid organ transplantation in the years to come.
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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 | 0.326 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.058 | 0.023 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.010 | 0.015 |
| 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".