Developing Guidance for Donor Intervention Randomized Controlled Trials: Initial Discussions From the Canada-United Kingdom 2022 Workshop
Bibliographic record
Abstract
BACKGROUND: Donor interventions, including medications, protocols, and medical devices administered to donors, can enhance transplantable organ quality and quantity and maximize transplantation success. However, there is paucity of high-quality evidence about their effectiveness, in part because of ethical, practical, and regulatory challenges, and lack of guidance about conduct of donor intervention randomized controlled trials (RCTs). METHODS: With the vision to develop authoritative guidance for conduct of donor intervention RCTs, we convened a workshop of Canadian-United Kingdom experts in organ donation and transplantation ethics, research, and policy to identify stakeholders, explore unique challenges, and develop research agenda to inform future work in this promising field. RESULTS: Donor intervention trials should consider perspectives of broad group of stakeholders including donors, transplant recipients, and their families; researchers in donation and transplantation; research ethics boards; and healthcare providers and administrators involved in donation and transplantation. Unique challenges include (1) research ethics (living versus deceased status of the donor at the time of intervention, intervention versus outcomes assessment in different individuals, harm-benefit analysis in donors versus recipients, consent, and impact on research bystanders); (2) outcome data standardization and linkage; and (3) regulatory and governance considerations. CONCLUSIONS: Donor intervention RCTs hold potential to benefit organ transplantation outcomes but face unique research ethics, outcome data, and regulatory challenges. By developing research agenda to address these challenges, our workshop was an important first step toward developing Canada-United Kingdom guidance for donor intervention RCTs that are poised to improve the quality and availability of transplantable organs.
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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 | 0.492 | 0.507 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.019 | 0.018 |
| Research integrity | 0.074 | 0.053 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".