Feasibility and Optimization of Donation Advisor: a Decision Support Tool for Deceased Organ Donation and Transplantation
Bibliographic record
Abstract
Background: This study aimed to evaluate the ability of Donation Advisor (DA), a validated clinical decision support tool that uses continuous monitoring, variability analysis, and predictive models, to (i) predict likelihood of successful donation after circulatory determination of death (DCD) before withdrawal of life-sustaining measures (WLSM), and (ii) describe ischemia during WLSM in DCD patients. Methods: Eligible patients were screened at the 5 sites where DA was implemented. DA reports were generated in real time but shown to clinicians after the donation was complete (noninterventional). Clinicians were interviewed for improvement of the tool. Results: We enrolled 34 donor patients in the study; 27 had DCD attempts and 20 proceeded to organ recovery. DA reports were generated before WLSM in all 27 attempted DCD patients (100%) while post-WLSM ischemia reports were generated in 26 of 27 DCD attempts (96%). Nineteen of 34 involved clinicians completed interviews, 10 from intensive care, and 9 from transplantation team members. Following a user-centered design approach, feedback was used to create 5 versions. Revisions included additions, removals, clarifications, and formatting changes; the number of revisions decreased with each amendment. The report's predictive scores were found to be useful by most practitioners (83%). We identified barriers and drivers to use the report in future practice, some of which may be addressed through improved education and awareness. Conclusions: DA can be deployed in real time during the DCD process. The usefulness and usability of the DA report has improved through user feedback; both barriers and drivers to implementation exist.
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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 | 0.035 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".