Return to work after kidney transplant: a systematic review
Bibliographic record
Abstract
BACKGROUND: Renal transplant is the gold standard treatment for patients with end-stage renal disease. Employment after transplant is an important marker of recovery and a key component of general well-being with important social implications. AIMS: To evaluate employment status after renal transplant and to investigate facilitators of and barriers to return to work for renal transplant patients. METHODS: We searched PubMed, Scopus and the Cochrane Library in March 2019 using the following algorithms: 'return to work' AND kidney AND transplant. Eligible studies were selected by two independent researchers. Quality assessment was performed using the following tools: International Narrative Systematic Assessment (INSA) and Newcastle-Ottawa Scale (NOS) for cross-sectional and cohort studies. RESULTS: The review included 18 papers: 10 cross-sectional studies, 6 cohort studies and 2 narrative reviews. The weighted mean percentage for return to work within 1 year was 39.4% (95% CI 39.3-39.6%). Employment status was influenced by modifiable and non-modifiable factors, such as pre-transplant employment, sociodemographic characteristics, clinical conditions and comorbidities, operative technique (invasive or not), type of transplants (living donor or cadaver), pre-transplant dialysis, psychosocial support, educational level and participation in education programmes. CONCLUSION: Return to work after kidney transplant is a dynamic process influenced by numerous factors. It is vital to implement multidimensional interventions focused on rehabilitation and influencing modifiable factors to improve return to work after kidney transplant. This systematic review updates knowledge in the field of transplant and of disability management.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".