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)].
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".