Hepatitis C positive organ transplantation to negative recipients at a multiorgan Canadian transplant centre: ready for prime time
Bibliographic record
Abstract
BACKGROUND: Transplantation offers the best survival for patients with end stage organ disease. Transplant of hepatitis C virus (HCV) nucleic acid test (NAT) positive organs into negative recipients is a novel strategy that can expand the donor pool. We aim to evaluate our centre's experience. METHODS: We preformed a retrospective review of anti-HCV NAT positive and negative organs into negative recipients transplanted over 27 months. Primary outcome was the success rate of eradication of HCV post-transplant. Secondary outcomes were rate of transmission of HCV, treatment adverse events, and graft failure. RESULTS: 33 anti-HCV positive organs were transplanted into negative recipients. 22 (66.7%) were NAT positive. Median recipients age was 49 years (interquartile range [IQR] 44.5-62.0) with the majority being males (57.6%). NAT positive organ transplantations included 16 kidneys, 3 livers, 1 kidney-pancreas, 1 liver-kidney, and 1 heart. The most common HCV genotype was 1a (59.1%). The median time to initiating therapy was 41.5 days. SVR12 was 100% in patients who finished therapy. There were no adverse events with therapy and no graft failure. CONCLUSIONS: Anti-HCV NAT positive organ transplantation into negative recipients is safe with excellent eradication rates and no significant adverse events or graft failure. This would expand donor pool to close the gap between supply and demand.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".