The impact of direct-acting antiviral agents on liver and kidney transplant costs and outcomes
Bibliographic record
Abstract
Direct-acting antiviral medications (DAAs) have revolutionized care for hepatitis C positive (HCV+) liver (LT) and kidney (KT) transplant recipients. Scientific Registry of Transplant Recipients registry data were integrated with national pharmaceutical claims (2007-2016) to identify HCV treatments before January 2014 (pre-DAA) and after (post-DAA), stratified by donor (D) and recipient (R) serostatus and payer. Pre-DAA, 18% of HCV+ LT recipients were treated within 3 years and without differences by donor serostatus or payer. Post-DAA, only 6% of D-/R+ recipients, 19.8% of D+/R+ recipients with public insurance, and 11.3% with private insurance were treated within 3 years ( P < .0001). LT recipients treated for HCV pre-DAA experienced higher rates of graft loss (adjusted hazard ratio [aHR] 1.34 1.85 2.10 , P < .0001) and death (aHR 1.47 1.68 1.91 , P < .0001). Post-DAA, HCV treatment was not associated with death (aHR 0.34 0.67 1.32 , P = .25) or graft failure (aHR 0.32 0.64 1.26 , P = .20) in D+R+ LT recipients. Treatment increased in D+R+ KT recipients (5.5% pre-DAA vs 12.9% post-DAA), but did not differ by payer status. DAAs reduced the risk of death after D+/R+ KT by 57% ( 0.19 0.43 0.95 , P = .04) and graft loss by 46% ( 0.27 0.54 1.07 , P = .08). HCV treatment with DAAs appears to improve HCV+ LT and KT outcomes; however, access to these medications appears limited in both LT and KT recipients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".