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Cumulative Risk for Developing End-Stage Renal Disease in the US Population

2002· article· en· W2157603500 on OpenAlexaff
Bryce Kiberd, Catherine M. Clase

Bibliographic record

VenueJournal of the American Society of Nephrology · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsMcMaster UniversityDalhousie University
Fundersnot available
KeywordsMedicinePopulationEnd stage renal diseaseLife expectancyProstate cancerDemographyCohortBreast cancerGerontologyDiseaseGynecologyCancerInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The individual risk of developing end-stage renal disease (ESRD) and its overall impact on life expectancy is not known. This study's objectives were to determine the effect of ESRD on life expectancy for a cohort of 20-yr-olds and to compare this impact to that of several cancers for which population-based screening programs exist. A computer simulation, stratified by race (white, black) and by gender was used to calculate cumulative lifetime risk of ESRD, life-years lost to ESRD, and cumulative Medicare payments for ESRD. Similar calculations were made for breast, prostate, and colorectal cancer. The cumulative lifetime risk of ESRD for a 20-yr-old black woman is 7.8%. Equivalent risks for black men are 7.3%, white men 2.5%, and white women 1.8%. Lost years of life attributable to ESRD are 1.09, 1.10, 0.40, and 0.32 yr for black women, black men, white men, and white women, respectively. In blacks, ESRD is responsible for nearly as much loss of life-years as breast cancer in women and more loss of life-years than colorectal or prostate cancer in men. In addition, treatment costs for ESRD in this population are many-fold more expensive than cumulative treatment costs of these cancers. Exploring new screening and treatment strategies may be warranted to prevent ESRD, particularly in the US black population.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.245
GPT teacher head0.411
Teacher spread0.166 · 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

Citations145
Published2002
Admission routes1
Has abstractyes

Explore more

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