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Inadequacy of cardiovascular risk factor management in chronic kidney transplantation – evidence from the <scp>FAVORIT</scp> study

2012· article· en· W2100530244 on OpenAlexaff
Myra A. Carpenter, Matthew R. Weir, Deborah Adey, Andrew A. House, Andrew G. Bostom, John W. Kusek

Bibliographic record

VenueClinical Transplantation · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsWestern University
FundersNIH Clinical CenterNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineInternal medicineHomocysteineRisk factorDiabetes mellitusTransplantationKidney diseaseBlood pressureKidney transplantationRenal functionObesityLipid profileSurgeryCholesterolEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Kidney transplant recipients (KTRs) have increased risk of cardiovascular disease (CVD). Our objective is to describe the prevalence of CVD risk factors applying standard criteria and use of CVD risk factor-lowering medications in contemporary KTRs. METHODS: The Folic Acid for Vascular Outcome Reduction in Transplantation study enrolled and collected medication data on 4107 KTRs with elevated homocysteine and stable graft function an average of five yr post-transplant. RESULTS: CVD risk factors were common (hypertension or use of blood pressure (BP) lowering medication in 92%, borderline or elevated low-density lipoprotein (LDL) or use of lipid-lowering agent in 66%, history of diabetes mellitus in 41%, and obesity in 38%); prevalent CVD was reported in 20% of study participants. National Kidney Foundation BP guidelines (BP <130/80 mmHg) were not met by 69% of participants. Uncontrolled hypertension (BP of 140/90 mmHg or higher) was present in 44% of those taking antihypertension medication; 18% of participants had borderline or elevated LDL, of which 60% were untreated, and 31% of the participants with prevalent CVD were not using an antiplatelet agent. CONCLUSION: There is opportunity to improve treatment and control of traditional CVD risk factors in kidney transplant recipients.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.089
GPT teacher head0.379
Teacher spread0.290 · 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

Citations54
Published2012
Admission routes1
Has abstractyes

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