Health-Related Quality of Life 10 Years after Liver Transplantation: A Longitudinal Retrospective Review
Bibliographic record
Abstract
As survival post-liver transplantation (LTx) improves, it becomes increasingly important to understand how long-term health-related quality of life (HRQOL) is impacted. This was a longitudinal review examining HRQOL measured by Pediatric Liver Transplant Quality of Life (PeLTQL) in children between 8-17 years who underwent LTx (1.4 [0.8–3.3] years) at least one year prior to assessment. Demographic, medical, anthropometric, and HRQOL data (self-reported and parent proxy) were retrospectively collected over four years (2014–2017) at annual LTx clinic visits. The study included 35 patients (18M, 17F) and their parents/guardians. Parent-proxy and child PeLTQL scores (total, subdomain) showed good to excellent agreement (p > 0.05) and did not change over four years (p > 0.05). Younger age (<12 years) and Caucasian ancestry were associated with higher parental and self-reported perceptions of HRQOL, respectively (future health, coping and adjustment, total scores). Parent perceived lower HRQOL in social–emotional sub-domain (p = 0.03) and the child reported lower sub-domain scores related to coping and adjustment (p = 0.04) when the child was noted to have co-morbid conditions related to mental health and neurocognitive development (25.7%). While child–parent perceptions of HRQOL in a multi-ethnic population of pediatric LTx recipients remain unchanged 10 years post-LTx, adolescents of non-Caucasian ancestry remain a population at risk for lower HRQOL.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".