The new SFB/TRR219 Research Centre
Bibliographic record
Abstract
A new Transregional Collaborative Research Center of the German Research Foundation (DFG) has been created to address reno-cardiovascular interactions underlying the increased cardiovascular risk in patients with chronic kidney disease to develop novel treatment strategies to reduce cardiovascular morbidity and mortality in these high-risk patients Chronic kidney disease (CKD) has developed into a serious global health problem. The prevalence of CKD is reported to be around 11% in high-income countries and the World Health Organization (WHO) estimated that around 1.5% of deaths worldwide were caused by CKD in 2012.1,2 Projections from the WHO predict that by 2030 CKD-related deaths will further increase by 15% compared to 2012.1,2 CKD patients have a five to ten fold higher risk of death than to progress to the end-stage of renal disease (ESRD = CKD Stage 5).1–4 Cardiovascular disease is the most common cause of death in patients with CKD Stages 3–5.5 Cardiovascular mortality significantly increases with severity of renal dysfunction6 and accounts for ∼40–50% of all deaths in patients with CKD stages 4-5 compared to 26% in humans with normal kidney function1,5 CKD-related cardiovascular death is mostly caused by ischaemic heart disease, accounting for over 50% of cardiovascular deaths in patients with CKD Stages 2–4, independent from the glomerular filtration rate (GFR).5 Sudden cardiac death becomes increases more as CKD progresses and is estimated to cause 60% of all cardiac deaths in dialysis patients.7 This suggests that at lower GFR CKD-specific pathological mechanisms significantly gain in importance. In addition, part of the cardiovascular deaths in CKD patients are due to cerebrovascular disease, valvular heart disease and arrhythmias.5
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.088 |
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".