Systematic review: hepatitis C viraemic allografts to hepatitis C‐negative recipients in solid organ transplantation
Bibliographic record
Abstract
BACKGROUND: Given the success of direct-acting antivirals (DAAs) in treating hepatitis C (HCV), interest is growing in utilizing solid organs from allografts with active HCV to expand donor availability. AIM: To review post-transplant outcomes and patient survival in HCV-negative recipients receiving solid organ transplants (SOT) from viraemic, that is, HCV+/NAT+ (nucleic acid testing) allografts. METHODS: A literature search was conducted on PubMed and EMBASE from 01/01/2007 to 4/17/2021 for articles matching eligibility criteria. Two authors independently screened titles and abstracts. Disagreements were solved by a third independent reviewer. Methodological quality assessment was done using a modified Newcastle-Ottawa scale (NOS). Data synthesis was done qualitatively using median, ranges and percentages. RESULTS: Thirty-five studies were included (or 852 SOTs): 343 kidney, 233 heart, 204 liver, and 72 lung transplants from viraemic allografts. Of the recipients eligible for sustained virological response at 12 weeks (SVR12) calculation, 100% achieved cure from HCV. No deaths/graft failures were reported to be related to HCV transmission. Seven SOT recipients had viral relapse, with all seven patients treated successfully. Four patients developed fibrosing cholestatic hepatitis with complete resolution post-treatment. CONCLUSIONS: Transplanting viraemic organs into uninfected individuals can become the standard of care for patients who do not have contraindications to DAAs.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".