Deceased Organ Donation in Canada: An Opportunity to Heal a Fractured System
Bibliographic record
Abstract
There has been no significant increase in the number of deceased organ donors in Canada over the past decade. Canada's donation and transplant system will be restructured with the formation of a new national organization to oversee activity in provincially governed donation and transplantation services. We review the current status of deceased organ donation, highlight issues contributing to the current stagnation in donation and identify changes that will enable success in a new Canadian system. Determining Canada's organ donation performance is difficult because the data required to calculate meaningful metrics of donation performance are not available. Canadians wait longer for transplantation than Americans, and Canada is falling further behind the United States primarily because of fewer donations after cardiac death. The ongoing divide between intergovernmental jurisdictional domains limits national initiatives to improve Canada's donation system. The success of a new national system will be enabled by uniform provincial legislation to ensure that all patients are offered the option to donate, commitment of resources to support organ donation by provincial governments, transparent reporting of comparable metrics of donation performance, establishment of processes to introduce and implement new initiatives and alterations to reimbursement models for organ donation and recovery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".