The Dialysis Outcomes and Practice Patterns Study (DOPPS) in Turkey
Bibliographic record
Abstract
INTRODUCTION: Turkey has one of the largest treated end-stage renal disease (ESRD) patient populations in Europe (N = 66,711). In 2013, the international Dialysis Outcomes and Practice Patterns Study (DOPPS), a prospective study of hemodialysis (HD) practices and outcomes, initiated data collection in Turkey. Here we provide comparisons of HD patients in DOPPS-Turkey with other international regions and with patients in the Registry of Turkish Nephrology, Dialysis and Transplantation. METHODS: DOPPS-Turkey study sites were randomly selected from all Turkish HD units treating ≥25 in-center chronic HD patients. Detailed patient- and facility-level data were collected for 20-30 randomly selected prevalent HD patients per facility. FINDINGS: Demographic and comorbidity profiles for DOPPS-Turkey patients were similar to HD patients overall in the 2013 Turkish Registry Report. In Turkey: diabetes was the most common ESRD cause (37%); arteriovenous fistula use was 83%; mean single pool Kt/V was 1.61. Compared with other international regions, Turkey had the highest mean hemoglobin (11.5 g/dL), ferritin (771 ng/mL), and interdialytic weight gain (3.28%), while Turkey had the lowest mean systolic blood pressure (127 mmHg) and erythropoiesis stimulating agent prescription (57%). Turkish patients also reported the highest depression scores. DISCUSSION: In this first DOPPS-Turkey report, the DOPPS sample agrees well with national Turkish Registry data. Treatment and laboratory data, and patient-reported outcomes, demonstrate similarities and previously unrecognized contrasts to DOPPS findings in Europe, Japan, and North America. Long-term follow-up of these patients will describe how these differences relate to clinical outcomes within Turkey.
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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 | 0.001 |
| 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".