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Record W2156968346 · doi:10.1093/ndt/gfq283

A systematic review of ethnic differences in the rate of renal progression in CKD patients

2010· review· en· W2156968346 on OpenAlexaff
Sean J. Barbour, Michael Schächter, Lee Er, Ognjenka Djurdjev, Anna S. Levin

Bibliographic record

VenueNephrology Dialysis Transplantation · 2010
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsFraser HealthSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney diseaseEthnic groupDialysisIntensive care medicineDiseaseRenal replacement therapyHemodialysisEnd stage renal diseaseDiabetes mellitusHealth careIncidence (geometry)GerontologyInternal medicineEndocrinologyEconomic growth

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a public health problem of increasing importance, consuming a growing proportion of health care resources. It is estimated that, in the United States, there are 19.2 million individuals with CKD [1], and this figure is expected to increase in parallel to the rising prevalence of hypertension and diabetes. By 2010, the projected number of end-stage renal disease (ESRD) patients will climb to over 650 000 [2], with a further predicted increase to 2.24 million by 2030 [3]. Therefore, improved understanding of the predictors of GFR decline is essential. Information about predictors would allow nephrologists to accurately predict those CKD patients at risk of progressing to ESRD, which would help to individualize patient care, allowing for optimal planning for renal replacement therapy (RRT), reduce the need for urgent dialysis and ultimately allow for more efficient allocation of scarce health care resources. In addition, the recognition of novel risk factors for progression may lead to the development of new therapies capable of altering the trajectory of the disease. It has been recognized that the incidence of ESRD is higher in ethnic minorities; however, the reasons for this have not been well defined. For example, American blacks are four times more likely to require dialysis than whites [4]. An increased burden of ESRD has also been shown to affect Hispanics and Asians [5]. Conversely, ethnic minorities tend, paradoxically, to have improved outcomes once started on haemodialysis [5,6]. Several mechanisms have been suggested to explain such differences, though these have not been proven. For example, a higher prevalence of co-morbidities, lower socioeconomic status and comparatively worse access to health care among ethnic minorities have been cited as reasons for the higher incidence of ESRD [7–9]. Such reasoning holds that the incidence of ESRD is dependent on the number of CKD patients at risk of progressing, and the higher prevalence of diabetes and hypertension in blacks may result in greater CKD [4]. Similarly, lower socioeconomic status in minorities may create inequalities in access to health care resources and thus reduce delivery of medical management known to slow the progression of renal dysfunction [10,11]. A second explanation is that a higher death rate amongst CKD patients in one ethnic group would leave fewer patients alive to require RRT, thus affecting the observed incidence rate of ESRD. Different thresholds for starting RRTwould also affect the measured incidence of ESRD. Finally, the higher incidence of ESRD amongst ethnic minorities may in fact be due to a faster rate of GFR decline and more rapid progression of renal disease. The purpose of this review is to summarize the available evidence on ethnic differences in the rates of CKD progression towards ESRD. Ideally, available studies would directly observe rates of GFR decline in CKD cohorts of different races. This would avoid such confounders as CKD prevalence, the competing risk of death and varying thresholds for starting RRT. Alternatively, in studies examining the incidence rates of ESRD, inferences can be made regarding progression rates only if attempts are made to account for differences in the baseline prevalence of CKD and in longitudinal mortality rates.

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)0.002
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.066
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.330
Teacher spread0.302 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations45
Published2010
Admission routes1
Has abstractyes

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