Recent updates in kidney risk prediction modeling: novel approaches and earlier outcomes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent years have witnessed the development of kidney risk prediction models which diverge from traditional model designs to incorporate novel approaches along with a focus on earlier outcomes. This review summarizes these recent advances, evaluates their pros and cons, and discusses their potential implications. RECENT FINDINGS: Several kidney risk prediction models have recently been developed utilizing machine learning rather than traditional Cox regression. These models have demonstrated accurate prediction of kidney disease progression, often beyond that of traditional models, in both internal and external validation. On the opposite end of the spectrum, a simplified kidney risk prediction model was recently developed which minimized the need for laboratory data and instead relies primarily on self-reported data. While internal testing showed good overall predictive performance, the generalizability of this model remains uncertain. Finally, there is a growing trend toward prediction of earlier kidney outcomes (e.g., incident chronic kidney disease [CKD]) and away from a sole focus on kidney failure. SUMMARY: Newer approaches and outcomes now being incorporated into kidney risk prediction modeling may enhance prediction and benefit a broader patient population. However, future work should address how best to implement these models into practice and assess their long-term clinical effectiveness.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".