Risk prediction in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review aims to describe the challenges and highlight recent advances in the field of risk prediction for patients with chronic kidney disease (CKD). We first focus on methods of model development and metrics of model performance in general, and then highlight important risk prediction tools for patients with CKD, for prediction of kidney failure and all-cause mortality. RECENT FINDINGS: Investigators have used data from patients with CKD stages 1-5 and developed models for predicting the progression to kidney failure and all-cause mortality. Models for kidney failure have included estimated glomerular filtration rate, albuminuria, demographic and laboratory variables, and have achieved excellent discrimination. In contrast, model performance for prediction of all-cause mortality has been relatively modest. No validated models exist for predicting the risk of cardiovascular events in patients with CKD. SUMMARY: Models for predicting kidney failure in patients with CKD are highly accurate and clinically usable. The kidney failure risk equation includes routinely collected laboratory data and can predict the progression of CKD to kidney failure with accuracy. Additional validation of the risk equation and development of new models for all-cause mortality and cardiovascular events in patients with CKD are needed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".