Population-wide eGFR percentiles in younger adults and clinical outcomes
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Identifying meaningful estimated glomerular filtration rate (eGFR) reductions in younger adults (<65 years) could guide prevention efforts. To aid in interpretation and identification of young adults at risk, we examined the association of population-level eGFR percentiles relative to the median by age and clinical outcomes. METHODS: We conducted a retrospective cohort study of 8.7 million adults from Ontario, Canada aged from 18 to 65 years from 2008 to 2021 with an eGFR measure (both single outpatient value and repeat measures). We calculated median eGFR values by age and examined the association of reduced eGFR percentiles (≤10th, 5th, 2.5th, and 1st) with outcomes using time to event models. Outcomes were a composite of all-cause mortality, major adverse cardiac outcomes (MACE) with/without heart failure (MACE+), and kidney failure as well as each component individually. RESULTS: From the age of 18 to 65, the median eGFR declined with age (range 128 to 90) and across percentiles [eGFR ranges 102 to 68 for ≤10th, 96 to 63 for ≤5th, 90 to 58 for ≤2.5th and 83 to 54 for 1st]. The adjusted rate for any adverse outcome was elevated at ≤10th percentile (HR 1.14 95%CI 1.10-1.18) and was consistent for all-cause mortality, MACE, MACE+, and predominant for kidney failure (HR 5.57 95%CI 3.79-8.19) compared to the median eGFR for age. Young adults with an eGFR in the lower percentiles were less likely to be referred to a specialist, have a repeat eGFR, or albumin to creatinine ratio measure. CONCLUSIONS: eGFR values at the 10th percentile or lower based on a population-level distribution are associated with adverse clinical outcomes and in younger adults (18 to 39) this corresponds to a higher level of eGFR that may be underrecognized. Application of population-based eGFR percentiles may aid interpretation and improve identification of younger adults at risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".