Clinical Outcomes with Antiviral Prophylaxis or Preemptive Therapy for Cytomegalovirus Disease after Liver Transplantation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives：We conducted a systematic review and meta-analysis to compare the clinical outcomes of patients after liver transplantation accepting antiviral prophylaxis (AP) or preemptive therapy (PT) for preventing cytomegalovirus (CMV) disease. METHODS: A literature search of PubMed, Cochrane, Embase was conducted up to June 1, 2016. References of the retrieved articles were also reviewed and relevant studies were included. The primary outcomes were incidence of CMV infection, incidence of CMV disease, mortality and opportunistic infection. The second outcomes were the mean time to CMV infection and CMV disease, adverse drug reaction (ADR). Sensitivity analysis and publication bias were evaluated. RESULTS: 6 cohort studies involving 1091 liver-transplant recipients (LTRs) were included. All studies were with high quality according to Newcastle-Ottawa Scales (NOS). Incidence of CMV infection and CMV disease showed significant difference between the AP and PT in high-risk patients. There was no significant difference of CMV-related mortality (725 patients, OR 1.27, 95%CI 0.12-13.47, p=0.84) and other opportunistic infections (311 patients, OR 0.85, 95%CI 0.49-1.45, p=0.55) in all "at-risk" patients between the two strategies, whereas late-onset CMV infection and CMV disease were found in patients receiving AP. CONCLUSION: We recommended the use of AP instead of PT in the high risk patients, and PT could be used in moderate or low risk patients for the similar clinical outcomes in preventing CMV disease. RCTs comparing the two strategies are warranted. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".