Epidemiology of end‐stage renal disease and hemodialysis treatment in <scp>S</scp>erbia at the turn of the millennium
Bibliographic record
Abstract
The study presents the epidemiological features of patients treated with renal replacement therapy (RRT) in Serbia from 1997 to 2009 and compares the results of hemodialysis treatment in 1999 and 2009. Epidemiological data were obtained from the National Registry of RRT patients and data on hemodialysis treatment from special surveys conducted in 1999 and 2009. Within the period 1997-2009 the incidence of patients on RRT increased from 108 to 179 per million population (pmp), prevalence rose from 435 to 699 pmp, while mortality rate fell from 20.7% to 16.7%. The frequency of patients with glomerulonephritis decreased, while that of patients with diabetes and hypertensive nephropathy increased. In late 2009 there were 5208 patients receiving RRT in Serbia. Within the examined period new hemodialysis and reverse osmosis equipment were purchased, high-flux dialyzers with synthetic membranes were increasingly used and the number of patients receiving hemodiafiltration increased to 17.6%. Kt/V greater than 1.2 was recorded in 16% of the patients in 1999 but 52% in 2009. Options for correction of anemia and mineral disorders have also improved. The percentage of patients with HbsAg (13.8% vs. 4.8%) as well as anti-hepatitis C virus antibodies positive patients (23.2% vs. 12.7%) was significantly lower in 2009 than in 1999. Both the incidence and prevalence of RRT patients in Serbia are rising continuously, while the mortality rate is falling. More favorable conditions for dialysis treatment have brought about significant improvement in the results over the last 10 years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".