Canadian organ donation organizations’ donor audit processes: an environmental scan
Bibliographic record
Abstract
PURPOSE: Deceased donor audits (DAs) allow organ donation and transplantation systems to measure and analyze missed donation opportunities (MDOs). Missed donation opportunities can harm both patients/families denied the opportunity to donate and patients on transplant waitlists denied access to lifesaving organs. In Canada, there are no national standards for DAs, data analysis, nor accountability processes surrounding MDOs. Understanding DA current practice in each jurisdicton would facilitate developing a national strategy for DAs. METHOD: All provincial organ donation organizations (ODOs) were invited to participate in an environmental scan (ES) of current DA practices. The two ES phases were an electronic survey followed by semistructured interviews. We collected information about the objectives, frequency, scope, data collection methodology, resources required, definitions/metrics used, and process for reporting outcomes. RESULTS: All eleven ODOs participated in both phases of the ES (July and October 2019). The primary purposes for conducting DAs were to estimate the following: 1) donor potential (5/11, 45%); 2) system performance at the provincial level (3/11, 27%); and 3) system performance at the hospital level (3/11, 27%). Frequency of DAs varied from weekly to annually, depending on the availability of death reports, urban vs rural setting, and staffing. High variability was observed in DA methodology, donor definitions, and metrics across jurisdictions. CONCLUSION: There is significant variability across Canadian ODOs in the methodology, definitions, timeliness, data collection, and reporting of DAs. This underscores the need for a national donor audit strategy to reduce preventable harm from MDOs to patients/families at end of life and those on transplant waitlists.
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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 | 0.041 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".