Development of a national minimum data set to monitor deceased organ donation performance in Canada
Bibliographic record
Abstract
PURPOSE: Deceased donation data requires standardization to enable accurate interprovincial and international comparisons of deceased donation performance. In Canada, most provincial organ donation organizations (ODOs) have developed different processes and infrastructures for referring potential donors and subsequent data collection. This has led to differing definitions of the performance measures used for each step in the donation process, from potential donor identification to consent to transplantation. The Deceased Donation Data Working Group (DDDWG), comprised of representatives from ODOs across Canada, was therefore convened by Canadian Blood Services to develop a national, comprehensive, standardized deceased donation minimum data set. METHODS: The DDDWG's scope encompassed considering all potential deceased organ donation data elements, including operational and performance data collected along the deceased donor pathway from donation potential to donation and disposition of organs. An environmental scan was conducted of other existing deceased donation registries from the Canadian and the international community. The DDDWG then engaged in regular face-to-face meetings and teleconferences to develop recommendations for the minimum data set that would satisfy key considerations, including the impact on existing ODO data collection processes, financial impact on stakeholders, the clinical and operational needs of multiple healthcare professionals involved in the deceased donation pathway, and availability of other existing national data sets that could be leveraged to reduce data collection burden. RESULTS: The key deceased donation data elements identified by the DDDWG are contained in an inverted pyramid framework that was derived from similar work conducted in other countries. CONCLUSION: The DDDWG developed recommendations for proposed definitions and data sources that should be adopted nationally to guide the collection of deceased donation data. The ultimate purpose of the final minimum data set is to harmonize and standardize donation data definitions in Canada and align with international standards; inform the development of operational and clinical practice standards at the provincial and national levels; develop a framework for deceased donation performance measures; and advance the science of deceased donation.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 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".