Safety and Biovigilance in Organ Donation (SAFEBOD): Protocol for a Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Tension lies between the need to increase access to organ transplantation and the equally urgent need to prevent inadvertent transmission of infectious diseases or cancer from organ donors. Biovigilance, or the evaluation of potential donors, is often time-pressured and may be based on incomplete information. OBJECTIVE: The Safety and Biovigilance in Organ Donation (SAFEBOD) study aims to improve estimates of infection and cancer transmission risk and explore how real-time data access could support decision-making. METHODS: We will link existing donor referral, actual donor, recipient, and health-outcome data sets from 2000-2015 in New South Wales. Organ donor data sets will include the Organ Donor Characterizing Risk-Profile of Donors Study, the National Organ Matching System, the Australian and New Zealand Organ Donor Register, and the Australian and New Zealand Living Donor Kidney Register. Recipient data sets will include the Australian and New Zealand Dialysis and Transplant Register, the Australian and New Zealand Cardiothoracic Register, the Australian and New Zealand Islet and Pancreas Register, and the Australian and New Zealand Liver Transplant Register. New South Wales health outcome data sets will include HIV and AIDS Notifications and Surveillance Data, the Notifiable Conditions Information Management System, Admitted Patient Data Collection, Emergency Department Data Collection, the Central Cancer Registry, and the Cause of Death Data Collection. We will link organ donors to transplant recipients and health outcomes data sets using probabilistic data-matching based on personal identifiers. Transmission and nontransmission events will be determined by comparing previous cases in donors and posttransplant cases in recipients. We will compare the perceived-risk at referral with the verified risk from linked health outcome data sets and the odds of cancer or contracting an infectious disease in organ recipients from donors based on their transmission-risk profile and estimate recipient survival by donor transmission risk group. RESULTS: Data were requested from each of the listed registries in September 2018, and data collection is ongoing. Linked data from all listed data sets are expected to be complete in September 2020. CONCLUSIONS: The SAFEBOD study will overcome current limitations in organ donation by accessing comprehensive information on referred organ donors and recipients in existing data sets. The study will provide robust estimates of disease transmission and nontransmission events based on recent data. It will also describe the agreement between perceived risk estimated at the time of referral and verified risk when all health outcome data are accessible. The improved understanding of transmission and nontransmission events will inform clinical decisions and highlight where current policies can be revised to broaden the acceptance of deceased donors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18282.
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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 | 0.058 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".