Contemporary risk prediction models in chronic kidney disease: when less is more
Bibliographic record
Abstract
PURPOSE OF REVIEW: Clinicians have an ever-increasing number of prediction tools at their disposal for estimating the risk of kidney failure in their patients. This review aims to summarize contemporary evidence for chronic kidney disease (CKD) risk prediction models across the spectrum of kidney function, and explore nuances in the interpretation of risk estimates. RECENT FINDINGS: A European study using predominantly laboratory data has extended kidney failure prediction to patients with more preserved estimated glomerular filtration rate. For older patients with advanced CKD, prediction tools that censor for death (such as the Kidney Failure Risk Equation) overestimate the risk of kidney failure, especially over time horizons longer than 2 years. This problem can be addressed by accounting for the competing risk of death, as shown in well designed validation studies. The clinical utility of kidney failure risk prediction tools is being increasingly tested at a population level to inform policy and referral guidelines. SUMMARY: There is welcome trend to validate existing prediction tools in diverse clinical settings and identify their role in clinical practice. Clinicians should be cognizant of overestimating kidney failure risk in older patients with advanced CKD due to the competing risk of death. For moderate CKD and for short-term predictions, the Kidney Failure Risk Equation remains the most widely validated prediction tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| 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".