A Distribution-Based Approach to Age Associations Between Continuous Kidney Function and Adverse Events in the General Population
Bibliographic record
Abstract
Background: Whether having a low eGFR value relative to age-based medians is associated with a higher risk of an adverse event is yet to be explored. We aim to derive continuous age-specific associations between percentiles (1st, 2.5th, 5th, 10th) of eGFR and any adverse outcome (first of death, cardiovascular events, end-stage kidney disease), and obtain corresponding eGFR values. Methods: We included 8.7 million adults (aged 18-65) with ≥1 eGFR value during January 2008-March 2020 in Ontario. Adjusted Cox models were used to estimate the association of a lower eGFR percentile (1st, 2.5th, 5th, 10th) and an adverse event by age from 18 to 65 relative to the age based median. Results: Overall eGFR values declined with age and the risk of an adverse event was higher with an eGFR in the 10th percentile of lower across all age groups (see Figure). In younger individuals, lower percentiles of eGFR occurred at higher eGFR cutoffs relative to older individuals and were associated with an elevated risk of an adverse event. For example, an eGFR in the lowest 5th percentile would occur < 93 ml/min at age 20, < 80 ml/min at age 40, and below 65 ml/min at age 60 with corresponding adjusted HRs for an adverse event of 1.42, 1.28 and 1.32, respectively. Conclusions: An age distribution-based approach to identifying lower eGFR values and their associated risk of an adverse event may improve care.Figure:: Trends in index estimated glomerular filtration rate (eGFR, in mL/min/1.73m2) and associated adjusted HRs of any adverse outcome (first of death, cardiovascular events, end-stage kidney disease) by continuous age
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".