Kidney transplantation from hepatitis C antibody-positive donors into hepatitis C-negative recipients: initial single-centre experience in the UK
Bibliographic record
Abstract
To the Editor, A discrepancy between supply and demand of organs for transplantation1 creates the need for organs with higher-risk donor characteristics. Organs from donors with hepatitis C (HCV) infection have been discarded in the past because of the likelihood of transmission and absence of effective treatment1. Some donors who initially tested positive for HCV antibodies were negative in RNA testing. These organs generally come from younger donors. The Cardiff Transplant Unit was the first centre in the UK to undertake transplantation of kidneys from HCV-infected donors into negative recipients2,3. The current study investigates the safety and effectiveness of a 12-week course of a direct-acting antiviral (DAA), once viraemia is detected, among recipients of an HCV-positive donor, and discusses the engagement with patients. Recipients were tested for viraemia post-transplant, and DAA was initiated when detected to be viraemic. Patients and public groups were consulted throughout the design and implementation, through meetings, an information booklet, a letter to patients on the waiting list, and open evenings.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".