Integrating eGFR and Kidney Replacement Therapy Criteria Within the Definition of Kidney Failure: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Guidelines define kidney failure based on initiation of maintenance kidney replacement therapy (KRT) or eGFR below 15 mL/min/1.73 m2 for over 90 days, but most kidney failure registries track the incidence and outcomes of people who receive KRT only. The population burden of kidney failure and outcomes of patients identified by eGFR criteria remain under-studied. Methods: Using population-based datasets from Alberta, Canada, we studied adults (≥18 years) who initiated maintenance KRT or had incident kidney failure defined by KRT or eGFR criteria between April 2008 and March 2019. People who met eGFR criteria for kidney failure were followed from cohort entry until death, initiation of KRT, or censoring (outmigration or March 31, 2021), to estimate the 5-year risks of KRT initiation and death without receiving KRT, and the rates of acute care utilization during follow-up. Results: The annual incidence was 212 per million population for KRT versus 293 for kidney failure, with larger incidence differences between KRT and kidney failure in older age and females. Among the 9691 incident kidney failure cases, 6216 (64.1%) were first identified by eGFR criteria. Within 5 years of meeting eGFR criteria, 34.0% died without KRT. Females were less likely to receive KRT, more likely to die without receiving KRT, and had higher acute care use. Conclusion: KRT registries may capture one-third of incident kidney failure cases, inaccurately record the timing of disease onset, and under-represent older adults and females who have worse outcomes. Incorporating eGFR measurements to expand kidney failure data collection initiatives could improve early disease identification, equity of healthcare planning, and outcome reporting for all affected individuals. Funding: Government Support – Non-U.S.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".