Renal transplantation vs hemodialysis: Cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND/AIM: Chronic renal insufficiency (CRI), diabetes, hypertension, autosomal dominant polycystic kidney disease (ADPKD) are the main reasons for starting dialysis treatment in patients having kidney function failure. At present, dialysis treatments are performed in about 4,100 patients at 46 institutions in Serbia, out of which 90% are hemodialyses. At end-stage renal disease (ESRD) the only correct selection is kidney transplantation. The basic aim of the planned research was to compare ratio of costs and effects (Cost Effectiveness Analysis - CEA) of hemodialysis and kidney transplantation in patients at ESRD. METHODS: As the main issue of treatment in patients from both groups the life quality measured by the validated McGill Questionary, was used. The study included 150 patients totally, divided into two groups. The study group consisted of 50 patients with kidney transplantation performed at the Clinical Center of Serbia and the control group consisted of 100 patients on hemodialysis at Clinical Center of Serbia, Clinical Hospital Center Zemun, Clinical Hospital Center "Zvezdara", Clinical Center Kragujevac and Health Center "Studenica", Kraljevo, comparable with respect to sex, age and length of treatment with the study group. RESULTS: Effect of kidney transplantation in relation to hemodialysis being selection of treatment is expressed in the form of incremental ratio of costs and effects (Incremental Cost-Effectiveness Ratio - ICER). It is clear from the enclosed tables that the strategy of kidney transplantation is far more profitable considering the fact that it represents saving of EUR 132,256.25 per one year of contribution Quality Adjusted Life Years (QALY) within the period of 10 years. According to all aspects of live quality (physical symptoms and problems, physical well-being, phychological symptoms, existential well-being and support), difference is statistically important in favour of transplant patents. CONCLUSION: The costs of patient therapy by hemodialysis at end-stage renal disease is far greater than by performing therapy of transplantation and maintenance, by almost three and a half times. Difference in total quality aspects of human life (physical, emotional, social, spiritual and financial) between dialysed and transplant patients is statistically significant and by 18.12% greater in transplant patients than in patients on hemodialysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".