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Record W2799552858 · doi:10.1111/ajt.14895

The impact of direct-acting antiviral agents on liver and kidney transplant costs and outcomes

2018· article· en· W2799552858 on OpenAlexaff
David A. Axelrod, Mark A. Schnitzler, Tarek Alhamad, Fredric D. Gordon, Roy D. Bloom, Gregory P. Hess, Haijun Xiao, Mustafa Nazzal, Dorry L. Segev, Vikas R. Dharnidharka, Abhijit S. Naik, Ngan N. Lam, Rosemary Ouseph, Bertram L. Kasiske, Christine M. Durand, Krista L. Lentine

Bibliographic record

VenueAmerican Journal of Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthLiver Center, Saint Louis UniversityMinneapolis Medical Research Foundation
KeywordsMedicineSerostatusHazard ratioInternal medicineHepatitis CHepatitis C virusGastroenterologyHuman immunodeficiency virus (HIV)ImmunologyConfidence intervalViral loadVirus

Abstract

fetched live from OpenAlex

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] 1.341.852.10, P < .0001) and death (aHR 1.471.681.91, P < .0001). Post-DAA, HCV treatment was not associated with death (aHR 0.340.671.32, P = .25) or graft failure (aHR 0.320.641.26, 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% (0.190.430.95, P = .04) and graft loss by 46% (0.270.541.07, 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. 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] 1.341.852.10, P < .0001) and death (aHR 1.471.681.91, P < .0001). Post-DAA, HCV treatment was not associated with death (aHR 0.340.671.32, P = .25) or graft failure (aHR 0.320.641.26, 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% (0.190.430.95, P = .04) and graft loss by 46% (0.270.541.07, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations53
Published2018
Admission routes1
Has abstractyes

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