Using Linked Health Service Data in Multimodal Modeling of Kidney Transplant Waitlist Outcomes: Protocol for the Maximizing Organ Donor Utility Systemwide (MODUS) Study
Bibliographic record
Abstract
BACKGROUND: Increasing deceased organ donation is a worldwide priority constrained by concerns of inadvertent transmission of cancer or infectious diseases from deceased organ donors. Up to 60% of potential donors referred for consideration for deceased organ donation in Australia do not proceed due to biovigilance concerns. OBJECTIVE: We aim to describe the impact of accepting or declining potential donors foregone for biovigilance concerns on patient and transplant outcomes. METHODS: The MODUS (Maximizing Organ Donor Utility Systemwide) study will use data for patients ever waitlisted for kidney transplantation and all potential donors referred for consideration for deceased organ donation. First, we will use binational data from the Australian and New Zealand Dialysis and Transplant Registry 2010-2020 to describe and evaluate factors impacting the current patient journey on the kidney transplant waitlist, including episodes of suspension and reactivation, time waiting, and whether transplanted. Second, we will quantify the time from offer decline to deceased donor transplantation and the impact of the intersectional disadvantage on the waiting time after decline for patients on the waitlist using flexible parametric survival models. Third, the MODUS study will use an established dataset of outcome data for all candidates for deceased organ donors referred to the New South Wales (NSW) Organ and Tissue Donation Service (OTDS) in 2010-2020 to describe donor referral risk profiles and determine any potential donor gains that could be made through better access to donor information at the time of decision-making, more accurate estimation of the absolute biovigilance risk, and varying of the acceptable biovigilance risk thresholds for accepting donors. Lastly, we will use the estimates derived from the first 3 aims as inputs for health economic models, where, using cohort- and individual patient-level simulations, we will quantify the impact of varying donor referral decisions on health care costs, quality-adjusted survival, the time on the waitlist, and the time to a kidney transplant. RESULTS: Linked health data were received in 2023. Data analysis is ongoing, and results will be disseminated at scientific conferences, published in the scientific media, and published via collaborator networks in 2025. CONCLUSIONS: The MODUS study will provide evidence of the individual-level and health service effects of increasing acceptance of deceased donor kidneys that would otherwise be declined due to biovigilance concerns. Specifically, we expect to report our findings on improvements in overall patient survival and quality of life by increasing the number of waitlisted people transplanted from donors with an acceptable biovigilance risk who are currently foregone. We will also report on the cost-effectiveness of a potential "informed biovigilance strategy" versus current practice. In doing so, we will develop evidence to support policy and complex clinical decisions in Australia's organ donor referral process with potential worldwide application. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67588.
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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 | 0.134 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".