Systematic review: impact of liver transplantation on employment
Bibliographic record
Abstract
Background: The majority of liver transplant recipients survive long term after the procedure. Aim: To assess if this positive outcome is associated with improved employment post-transplant. Methods: A systematic review of publications between 2001 and 2016 was performed. A standard procedure was used to search for suitable publications from two databases (PubMed and EMBASE). Duplicates were removed and abstracts screened by both authors for possible inclusion. Possible suitable publications were obtained and examined for the presence of pre- and post-employment information. Full articles that had this information were reviewed by standard methodology for assessment of bias. Results: A total of 162 individual abstracts were screened. Thirty-five full papers were reviewed and 13 papers included in the detailed review. Risk of bias was considered high due to low response rates, poor assessment of prognostic and confounding factors and varying definitions of employment. Heterogeneous data precluded meta-analysis. Eight studies focused on return to work as a primary outcome and five on quality of life with employment as a secondary outcome. Follow-up varied between 2 and 13 years. Rates of employment fell in all studies assessed. Employment rates ranged from 26 to 80% pre-transplant and 18 to 44% post-transplant. The proportion of those categorized as ill-health retired was 24% greater after orthotopic liver transplantation. Conclusions: Improved survival after liver transplantation was not reflected in a return to employment and retirement was common. Areas for further study include interventions to minimize physical deconditioning, depression associated with lower employment rates and type of work available after transplant.
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 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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".