Quality improvement tools to manage deceased organ donation processes: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To collate and summarise the literature on the quality improvement tools that have been developed for deceased organ donation processes after circulatory determination of death and neurological determination of death. DESIGN: Scoping review using the Joanna Briggs Institute framework. DATA SOURCES: We searched for published (MEDLINE, Embase, PsycINFO, CINAHL, Web of Science) and unpublished literature (organ donation organisation websites worldwide). The search was initially conducted on 17 July 2021 and updated on 1 June 2022. Included articles discussed the creation and/or use of quality improvement tools to manage deceased organ donation processes. Two independent reviewers screened the references, extracted and analysed the data. RESULTS: 40 references were included in this review, and most records were written in English (n=38), originated in Canada (n=21), published between 2016 and 2022 (n=22), and were specific for donation after neurological determination of death (n=20). The tools identified included checklists, algorithms, flow charts, charts, pathways, decision tree maps and mobile apps. These tools were applied in the following phases of the organ donation process: (1) potential donor identification, (2) donor referral, (3) donor assessment and risk, (4) donor management, (5) withdrawal of life-sustaining measures, (6) death determination, (7) organ retrieval and (8) overall organ donation process. CONCLUSIONS: We conducted a thorough investigation of the available quality improvement tools for deceased organ donation processes. The existing evidence lacks details in the report of methods used for development, testing and impact of these tools, and we could not locate tools specific for some phases of the organ donation process. Lastly, by mapping existing tools, we aim to facilitate both clinician choices among available tools, as well as research work building on existing knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.253 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.038 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".