An Exception to the Rule or a Rule for the Exception? The Potential of Using HIV-Positive Donors in Canada
Bibliographic record
Abstract
Selected human immunodeficiency virus (HIV)-infected patients with end organ failure can safely receive an organ transplant from an HIV uninfected donor. Recent demonstration of the short term safety of organ transplantation between HIV-infected persons prompted a change in US American law to allow such transplantations. Prompted by the recent completion of the first organ transplantation between HIV-infected persons in Canada, we review Canadian law regarding the use of organs from HIV-infected donors, estimate the number of potential HIV-infected donors in Canada, and critically review considerations related to advancing organ transplantation from HIV-infected donors in Canada. Existing legislation allows organ transplantation from an HIV-infected donor under exceptional medical circumstances and therefore no change in legislation is required to increase utilization of organs from HIV-infected donors for transplantation in Canada. Among 335,793 hospital deaths between 2005 and 2009 in Canadian provinces excluding Quebec, 39 potential HIV-infected donors were identified. The actual number of HIV potential donors is estimated to be approximately 60% lower (3-5 potential donor per year), if the absence of viremia is required for transplantation. Although offering all Canadians the opportunity to donate organs is a laudable goal, further research to understand the need for HIV-positive donors and the willingness of HIV-positive recipients to accept organs from HIV-positive donors is needed to inform future policy regarding organ donation from HIV-infected persons in Canada.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".