Transplantation of hepatitis C virus infected kidneys into hepatitis C virus uninfected recipients
Bibliographic record
Abstract
Long wait times for kidney transplant and the high risk of mortality on dialysis have prompted investigation into strategies to increase organ allocation and decrease discard rates of potentially viable kidneys. Organs from hepatitis C virus (HCV) antibody positive donors are often rejected; nearly 500 HCV-infected kidneys are discarded annually in the United States. Due the opioid epidemic, the number of HCV-infected donors has increased because of a rise in both new HCV infections and drug-related deaths. In the past 5 years, HCV has been transformed into a curable illness with direct-acting antiviral therapies (DAAs) that are effective in >95% of patients treated and are extremely well tolerated. Recent data has shown several direct-acting antiviral combinations are safe and effective after kidney transplant, and can achieve the same high cure rate seen in the general population and without increasing the rate of acute rejection. Because of this, strategies to decrease discard of HCV-infected organs have been devised. Two recent studies have transplanted HCV-uninfected dialysis patients with kidneys from donors actively infected with HCV; recipients were treated with DAA in the peri-transplant period. More research is needed to determine the safety and efficacy of this approach, but it has the potential to dramatically increase the donor pool of available kidneys, shorten waitlist times and ultimately decreases mortality in patients waiting for kidney transplant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".