Liver transplant outcomes in HIV-infected patients: a systematic review and meta-analysis with synthetic cohort
Bibliographic record
Abstract
OBJECTIVES: The relative success of liver transplantation in those with HIV compared to HIV-uninfected individuals remains a point of intense debate. We aimed to evaluate the effectiveness of liver transplantation in HIV-hepatitis co-infected patients using a meta-analysis and individual patient data meta-analysis as a synthetic cohort. METHODS: We searched MEDLINE via PubMed, EMBASE, Cochrane CENTRAL, AIDSLINE (inception to 2010), AMED, CINAHL, TOXNET, Development and Reproductive Toxicology, Hazardous Substances Databank, Psych-info and relevant conferences. We included cohort studies and individual case-reports evaluating survival of co-infected transplant patients. We abstracted data on cohort and case demographics and outcomes. We pooled cohorts using a random-effects analysis and created a synthetic cohort of cases using individual patient data. We confirmed this with the pooled cohort analysis. RESULTS: We included 15 cohort studies and 49 case series with individual patient data. At 12 months, 84.4% [95% confidence interval (CI) 81.1-87.8%] of patients had survived. Within the HIV-infected population evaluated, HIV-hepatitis B virus (HBV) co-infection was associated with optimal survival. In an adjusted model, individuals positive for HBV were 8.28 (95% CI 2.26-30.33) times more likely to survive when compared to those without HBV. Further, individuals with an undetectable HIV viral load at the time of transplantation were 2.89 (95% CI 1.41-5.91) times more likely to survive when compared to those with detectable HIV viremia. Hepatitis C virus was not a predictor of patient survival when adjusted for by other key predictors [0.54 (95% CI 0.17-1.80)].
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".