#2996 EARLY DECLINES IN KIDNEY FUNCTION AND ADVERSE OUTCOMES IN YOUNG ADULTS
Bibliographic record
Abstract
Abstract Background and Aims Whether early eGFR declines (eGFR below age-expected values) in younger adults are associated with adverse outcomes is unknown. We aim to estimate the association of an early eGFR decline with adverse outcomes by age group (18-39, 40-49, 50-65 years). Methods We included 8.9 million adults (aged 18-65) with ≥1 eGFR value using linked healthcare datasets in Ontario from January 2008-March 2020. The association of eGFR categories from <60 to >120 mL/min/1.73 m2 and adverse outcomes (death, cardiovascular outcomes, end-stage kidney disease) was examined using adjusted Cox models. Comparisons were relative to age normalized measured GFR categories (100-110 mL/min for 18-39, 90-100 mL/min for 40-49, 80-90 mL/min for 50-65). Results The mean age, eGFR and median follow up were 41 years, 104 mL/min and 9.2 years, respectively. 17.3%, 18.9%, and 17.7% had an eGFR below normal for ages 18-39, 40-49, and 50-65, respectively. The risk of an adverse event increased in a stepwise manner with eGFR values below the referent and occurred at higher eGFR values in those 18 to 39 [eGFR 70-80, age 18-39: incidence 4.37 events per 1000 person-years[p-y], HR 1.54(1.46-1.61); age 40-49: incidence 9.78 per 1000 p-y, HR 1.18(1.15-1.21); age 50-65: incidence 24.0 per 1000 p-y, HR 1.11(1.10-1.12)] (see Figure 1). Results persisted for each outcome individually, and after using repeated eGFR, using a common referent, and adjusting for multiple covariates. Conclusion Young adults (18-39) with an early eGFR decline were at a higher risk of adverse events and this occurred at higher eGFR levels relative to middle-aged and older adults.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".