V-213.5: Donor audits in deceased organ donation: A scoping review
Bibliographic record
Abstract
Background: Organ transplantation is a cost-effective treatment for organ failure, but a significant gap exists between the number of available organs and the demand for transplants. Donor audits (DA) have been proposed as a tool to identify barriers in the deceased organ donation process and guide quality improvement efforts. However, there is limited comprehensive evidence on the use and impact of DA in clinical settings. Therefore, in this study we sought to collate and summarize existing literature on DA and how they have been used to guide deceased organ donation and transplantation system performance and quality assurance. Methods: This scoping review followed the Joanna Briggs Institute (JBI) guidance and PRISMA-ScR reporting standards. We conducted a systematic search of the literature on MEDLINE, Cumulative Index of Nursing and Allied Health Literature, and Web of Science supplemented by Google on 6 May 2022. We aimed to search studies published after 1995 in English, French, and Spanish. Eligible studies included that reporting on DA focusing on estimating potential and actual deceased organ donors in various healthcare settings. Results: From 2,416 unique citations, 52 studies met the inclusion criteria. The majority focused on estimating potential donors and quantifying actual donors, highlighting missed donation opportunities, with most studies conducted in the UK and published between 2001 and 2006. Motivations for DA included enhancing donation programs and guiding quality improvement efforts. Barriers to donation included family decline and failure to identify potential donors. Quality improvement initiatives suggested include enhancing healthcare professionals’ education and improving donor management protocols. Conclusion: DA provides valuable insights into deceased organ donation programs and can help identify missed donation opportunities and barriers in clinical settings. Strategies to address these barriers, such as improving family approaches and strengthening donation practices, may enhance access to organ transplants. Further research is needed to assess the efficacy of DA in improving organ donation rates and transplant outcomes. This study has been funded by Canadian Blood Services.
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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 | 0.027 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.039 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".