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Prevalence of Low Glomerular Filtration Rate in Nondiabetic Americans

2002· article· en· W2108598216 on OpenAlexaff
Catherine M. Clase, Amit X. Garg, Bryce Kiberd

Bibliographic record

VenueJournal of the American Society of Nephrology · 2002
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsDalhousie UniversityWestern UniversityMcMaster University
Fundersnot available
KeywordsRenal functionMedicineConfidence intervalNational Health and Nutrition Examination SurveyPopulationCreatinineKidney diseaseUrologyInternal medicineDemographyEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.262
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations223
Published2002
Admission routes1
Has abstractyes

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