Organ Donation and Transplantation Registries Across the Globe: A Review of the Current State
Bibliographic record
Abstract
BACKGROUND: The current landscape of organ donation and transplantation (ODT) registries is not well established. This narrative review sought to identify and characterize the coverage, structure, and data capture of ODT registries globally. METHODS: We conducted a literature search using Ovid Medline and web searches to identify ODT registries from 2000 to 2023. A list of ODT registries was compiled based on publications of registry design, studies, and reports. Extracted data elements included operational features of registries and the types of donor and recipient data captured. RESULTS: We identified 129 registries encompassing patients from all continents except Antarctica. Most registries were active, received funding from government or professional societies, were national in scope, included both adult and pediatric patients, and reported patient-level data. Registries included kidney (n = 99), pancreas (n = 32), liver (n = 44), heart (n = 35), lung (n = 30), intestine (n = 15), and islet cell (n = 5) transplants. Most registries captured donor data (including living versus deceased) and recipient features (including demographics, cause of organ failure, and posttransplant outcomes) but there was underreporting of other domains (eg, donor comorbidities, deceased donor referral rates, waitlist statistics). CONCLUSIONS: This review highlights existing ODT registries globally and serves as a call for increased visibility and transparency in data management and reporting practices. We propose that standards for ODT registries, a common data model, and technical platforms for collaboration, will enable a high-functioning global ODT system responsive to the needs of transplant candidates, recipients, and donors.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".