Donor audits in deceased organ donation: a scoping review
Bibliographic record
Abstract
PURPOSE: We sought to collate and summarize existing literature on donor audits (DA) and how they have been used to guide deceased organ donation and transplantation system performance and quality assurance. SOURCE: We searched MEDLINE, Cumulative Index of Nursing and Allied Health Literature, and Web of Science supplemented by Google to identify grey literature on 6 May 2022, to locate studies in English, French, and Spanish. The data were screened, extracted, and analyzed independently by two reviewers. We grouped the results into five categories: 1) motivation for DA, 2) DA methodology, 3) potential and actual donors, 4) missed donation opportunities, and 5) quality improvement. PRINCIPAL FINDINGS: The search yielded 2,416 unique publications and 52 were included in this review. Most studies were from the UK (n = 13) and published between 2001 and 2006 (n = 15). The methodologies described for DA were diverse. Our findings showed that the primary motivation for conducting DA was to identify potential donors and the number of potential deceased organ donors is significantly higher than the number of actual donors. Among retrieved studies, the proportion of donation opportunities following neurologic determination of death was 95/222 (43%) compared with 25/181 (14%) for donation after cardiocirculatory death (DCD), suggesting that the missed donation rate is higher for DCD. CONCLUSION: Donor audits help identify missed donation opportunities along the deceased donation pathway and can help support the evaluation of quality improvement initiatives.
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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 | 0.056 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.037 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".