29 What Effect Does Liver Transplant in Adults have on Employment? - A Systematic Review
Bibliographic record
Abstract
Objectives Return to work after liver transplantation was last reviewed in 2000. Changes have occurred to transplant programmes since that time. As such, a review of recent publications was undertaken to identify if the employment experience has altered in the past 12 years. Methods A literature search was performed in PubMed and Embase. Papers published from January 2000 to December 2011 in the English language were included in the review. 181 papers were identified. After removal of duplicates 117 abstracts and titles were screened and 29 papers considered for eligibility. 24 papers were included in this review. Results 10 papers were from the USA and 14 were from other countries. The number of cases followed up ranged from 8 to 471 (total 3,222) with a mean age of 51.9 years. The follow-up period ranged from 2 to 20 years (mean 6.1 years). 10 studies included pre and post-transplant employment rates. Pre-transplant rates ranged from 40–75% (mean 63.8%). Post-transplant the rates ranged from 22–57% (mean 37.1%). In 9 studies the employment rate fell. Post-transplant employment rates fell with duration of follow-up (7 studies; p = 0.016). Cross-sectional analysis showed recipients had lower physical component scores on SF-36 compared to the general population. Longitudinal data showed improvement in physical function between pre- and post-transplant assessment. The results were lower than the general population but better than those with chronic liver disease. One paper identified depression as a factor associated with higher unemployment post-transplant. Conclusions The review indicates that post transplantation employment rates are lower compared to pre-transplantation rates, despite improved physical function. Rates appear to fall with duration of follow-up over the first 6.5 years after transplantation. A study with follow-up at intervals after transplantation would improve understanding of the return to work issues and help plan suitable interventions.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".