Health‐related quality of life after pediatric liver transplantation: A systematic review
Bibliographic record
Abstract
With improved survival rates after pediatric liver transplantation (LT), attention is targeting improving the health-related quality of life (HRQOL) as an outcome metric. We conducted a systematic review of the literature to examine HRQOL after pediatric LT, focusing on assessment tools and factors associated with HRQOL. A literature search was conducted through PubMed, Web of Science, Ovid, and Google Scholar for all studies matching the eligibility criteria between January 2004 and September 2016. Titles and abstracts were screened independently by 2 authors and consensus for included studies was achieved through discussion. A total of 25 (2 longitudinal, 23 cross-sectional) studies were reviewed. HRQOL in pediatric LT recipients is lower than healthy controls, but it is comparable to children with chronic diseases or other pediatric solid organ transplant recipients. Domain scores were lowest in school functioning on the Pediatric Quality of Life Inventory (PedsQL) Generic Core Scale 4.0 and general health perception on the Child Health Questionnaire, the 2 most commonly used generic HRQOL instruments. Identified predictors of poor HRQOL include sleep disturbances, medication adherence, and older age at transplantation. Two recently validated disease-specific HRQOL tools, Pediatric Liver Transplant Quality of Life tool and the Pediatric Quality of Life Inventory 3.0 Transplant Module, have enabled enhanced representation of patient HRQOL, when used in conjugation with generic tools. Heterogeneity in study design and instruments prevented a quantitative, meta-analysis of the data. In conclusion, continued optimization of durable outcomes for this population mandates prioritization of research focusing on the gap of targeted intervention studies aimed at specific HRQOL subdomains and longitudinal studies to predict the trajectory of HRQOL over time. Liver Transplantation 23 361-374 2017 AASLD.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".