P.25: Adaptive platform trial for deceased organ donation optimisation: OPTIDON APT.
Bibliographic record
Abstract
Background: Despite global efforts to improve deceased organ donation, there is substantial variability in donor- and organ- management practices, and hence organ utilisation. We propose a unified, international, multi-centre adaptive platform trial (APT) designed to efficiently evaluate and optimise pre- and post-retrieval interventions in deceased potential donors. Objective: To develop and implement a scalable APT that improves the number and quality of organs successfully transplanted from deceased donors. Methods: OPTIDON APT will use a pragmatic, Bayesian adaptive framework to evaluate pre- and post-retrieval interventions, grouped into domains based on intervention mechanism (e.g. pharmaceutical, artificial perfusion, donor blood pressure) and organ effect (e.g. hormonal resuscitation for heart donors, machine perfusion for individual organs). Such platform structures allow for new domains and/or interventions to be added and ineffective interventions dropped without starting new trials.Randomisation can occur at the donor (pre-retrieval) and/or organ level (post-retrieval). The trial will leverage existing intensive care, donation, and transplant registries, including donor-recipient linkage. The primary outcome will be successful transplantation (number and function), with secondary outcomes including graft survival, donor metrics, and protocol adherence. Collaboration and Governance: The platform will be globally scalable and coordinated through shared protocols, centralised analysis, and aligned outcome definitions. International partnerships will include donation and transplantation organisations and clinical trials networks across the UK, Canada, Spain, and Australasia. Governance will involve a trial steering committee, safety monitoring, and domain-specific expert groups. Local ethical standards would be applied for research in deceased donation and organ interventions. Impact: This research methodology has the potential accelerate evidence generation in donor management, improve graft outcomes, and enhance international collaboration. The platform structure enables rapid adaptation to emerging interventions, supporting a sustainable path to evidence-based deceased donor optimisation.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".