Impact of age on glomerular filtration estimates
Bibliographic record
Abstract
BACKGROUND: Glomerular filtration decreases progressively with age in adults. Predictive equation should have proper modelling to adequately account for normal senescence. METHODS: Corrected 24-h creatinine clearances (CCLs) were measured in a cohort of 773 outpatients from 18 to 90 years old. Multiple linear regression was used to model the effect of age on glomerular filtration. Comparisons were made with the simplified MDRD and the MAYO equations. Impact of the derived equation was tested in a second cohort of 7551 patients with normal serum creatinine. RESULTS: While all equations show declining function with age, our results suggest that the GFR reduction is progressive after the age of 30 and continue to decline steadily after the age of 60. This leads to a convex curve in the multiple regression analysis that is best fitted by an equation including the quadratic term (age(2)). In contrast, the MDRD equation produces a faster decrease in early adulthood and a flatter curve after the age of 60 while the MAYO equation produces a more linear effect. MDRD results in the normal range are lower than those estimated by the MAYO equation. These equations, as applied on an independent cohort of 7551 normal outpatients from 18 to 102 years, produce different profile of evolution of GFR with age. CONCLUSIONS: Inclusion of a quadratic term for age in the formula estimating GFR results in better modelling of the natural decline of renal function associated with ageing. Furthermore, as GFR steadily declines after the age of 30, a single cut-off value of GFR normality for all ages leads to underdiagnosis of young adults and over diagnosis of elderly individuals. Guidelines should take into account the observed reduction of kidney function with age in normal population for optimal evaluation of eGFR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".