Prevalence of Low Glomerular Filtration Rate in Nondiabetic Americans
Bibliographic record
Abstract
End-stage renal disease is an important and costly health problem. Strategies for its prevention are urgently needed. Knowledge of the population-based prevalence of renal insufficiency in nondiabetic adults would inform such strategies. Black and white nondiabetic adult participants in the Third National Health and Nutrition Examination Survey were analyzed. The analysis was stratified by age, gender, and race, and four clinically applicable methods were used to assess renal function. There were 13,251 complete records for analysis. By the Modification of Diet in Renal Diseases (MDRD) GFR (GFR) prediction Equation 7, 58% (95% confidence interval [CI], 56 to 60%) of the total adult nondiabetic black and white US population had MDRD GFR below 80 ml/min per 1.73m(2), 13% (95% CI, 11 to 14%) below 60 ml/min per 1.73m(2), and 0.26% (95% CI, 0.19 to 0.33%) below 30 ml/min per 1.73m(2). By the Cockcroft-Gault formula, the equivalent figures were 39% (95% CI, 37 to 41%), 14% (95% CI, 12% - 16%), and 0.81% (95% CI, 0.46 to 1.2%), respectively. The findings of an unexpectedly high prevalence of low clearance and the increased prevalence of low clearance with age were consistent across the four clearance estimation methods used and for each race-sex stratum. The absolute magnitude of the prevalence of low clearance was, however, dependent on the clearance method used. Assessed by estimation from serum creatinine, low clearance may be very common, particularly with advancing age. The prognosis (in terms of risk for progression and end-stage renal disease) of low clearance in unreferred populations may differ from that in referred populations and requires further study.
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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 | 0.002 |
| 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".