Improving the prognosis of patients with severely decreased glomerular filtration rate (CKD G4+): conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Patients with severely decreased glomerular filtration rate (GFR) (i.e., chronic kidney disease [CKD] G4+) are at increased risk for kidney failure, cardiovascular disease (CVD) events (including heart failure), and death. However, little is known about the variability of outcomes and optimal therapeutic strategies, including initiation of kidney replacement therapy (KRT). Kidney Disease: Improving Global Outcomes (KDIGO) organized a Controversies Conference with an international expert group in December 2016 to address this gap in knowledge. In collaboration with the CKD Prognosis Consortium (CKD-PC) a global meta-analysis of cohort studies (n = 264,515 individuals with CKD G4+) was conducted to better understand the timing of clinical outcomes in patients with CKD G4+ and risk factors for different outcomes. The results confirmed the prognostic value of traditional CVD risk factors in individuals with severely decreased GFR, although the risk estimates vary for kidney and CVD outcomes. A 2- and 4-year model of the probability and timing of kidney failure requiring KRT was also developed. The implications of these findings for patient management were discussed in the context of published evidence under 4 key themes: management of CKD G4+, diagnostic and therapeutic challenges of heart failure, shared decision-making, and optimization of clinical trials in CKD G4+ patients. Participants concluded that variable prognosis of patients with advanced CKD mandates individualized, risk-based management, factoring in competing risks and patient preferences.
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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 | 0.090 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".