Employment of Patients With Kidney Failure Treated With Dialysis or Kidney Transplantation - A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Patients with kidney failure treated with dialysis or kidney transplantation experience difficulties maintaining employment due to the condition itself as well as the treatment. We aimed to establish the rate of employment before and after initiation of dialysis and after kidney transplantation and to identify predictors of employment during dialysis and post-transplant.Methods: This systematic review and meta-analysis was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis, PRISMA, for studies that included employment rate in adults receiving dialysis or a kidney transplant. The literature search included cross sectional or cohort studies published in English in the period from January 1966 to August 2020 in the databases PubMed, Embase, and Cochrane Library. Data of employment rate, study population, age, gender, educational level, dialysis duration, kidney donor, ethnicity, dialysis modality, waiting time for transplantation, diabetes, and depression were extracted. Quality assessment was performed using the Newcastle-Ottawa Scale. Meta-analysis for predictors for employment and odds ratio; confidence intervals; and test for heterogeneity were calculated using Chi-squared statistics and I2. PROSPERO registration number: CRD42020188853.Results. 33 studies with 162,059 participants during dialysis and 31 studies with 137,742 participants receiving kidney transplantation. Dialysis patients were on average 52.6 years old (range 16-79), 60.3% males and kidney transplant patients 46.7 years old (range 18-78), 59.8% males. The employment rate (weighted mean) for dialysis patients was 26.3% (range 10.5-59.7%); pre-transplant 36.9% (range 25-86%), and post-transplant 38.2% (range 14.2-85%). Predictors for employment during dialysis and post-transplant were male, non-diabetic, peritoneal dialysis, and higher educational level, and post-transplant: pre-transplant employment, younger age, transplantation with a living donor kidney, and without depression.Conclusions: Patients with kidney failure had a low employment rate during dialysis, pre- and post-transplant. Kidney failure patients should be supported through a combination of clinical and social measures to ensure they remain in work.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".