Lifetime Probabilities of Needing an Organ Transplant Versus Donating an Organ After Death
Bibliographic record
Abstract
The lifetime probabilities of becoming a deceased organ donor and requiring or receiving an organ transplant are unknown. An actuarial analysis was performed in a representative Canadian sample. Using Canadian organ donation data 1999-2007, provincial waiting list and population census data, actuarial rates were produced that provide the probabilities, by age band and gender, of (1) becoming a deceased organ donor, (2) needing an organ transplant and (3) receiving all organs needed. Regardless of age, the lifetime probability of needing a transplant for males is approximately twice that of females. Depending on age, Canadians are five to six times more likely to need an organ transplant than to become a deceased organ donor. The lifetime probabilities of not receiving a required organ transplant, expressed as a percentage of individuals on the waiting list, ranges from approximately 30% at birth, 20 years and 40 years to approximately 40% at 60 years. Across provinces and genders, Canadians at all ages are much more likely to need an organ transplant than to become an organ donor. Approximately one-third of those in need of a transplant will never receive one. How this information may influence organ donation decisions is currently under study.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".