Bibliographic record
Abstract
PURPOSE OF REVIEW: Lifetime risk of outcomes is emerging as a highly relevant health indicator, even in the context of low absolute risk of disease progression in short time frames. Evidence to support this concept for kidney failure is increasing, with growing emphasis on the long-term impact of risk factors occurring early in life. RECENT FINDINGS: Proteinuria and stage of chronic kidney disease (CKD) are now established predictors of CKD progression in children, and youth with type 2 diabetes are emerging as a group at significant risk. Recent population-based studies have also examined the lifetime risk of end-stage renal disease in individuals with any childhood CKD. A recent study found that even in the absence of biomarkers of renal injury, childhood CKD can increase the lifetime risk of end-stage renal disease four-fold, and up to 10-fold in adults less than 40 years of age. SUMMARY: Children with CKD are at high lifetime risk of kidney failure and require follow-up. Identifying children at highest lifetime risk through the use of biomarkers and risk equations, and determining the optimal duration and intensity of follow-up requires further research.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".