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Record W2166460234 · doi:10.2215/cjn.05450513

Biomarkers of Vascular Calcification and Mortality in Patients with ESRD

2014· article· en· W2166460234 on OpenAlexaff
Julia J. Scialla, W.H. Linda Kao, Ciprian Crainiceanu, Stephen M. Sozio, Pooja C. Oberai, Tariq Shafi, Josef Coresh, Neil R. Powe, Laura Plantinga, Bernard G. Jaar, Rulan S. Parekh

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2014
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoUniversity Health Network
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteAgency for Healthcare Research and Quality
KeywordsMedicineOsteoprotegerinHazard ratioInternal medicineDialysisProportional hazards modelDiabetes mellitusConfidence intervalComorbidityMatrix gla proteinCalcificationCardiologyEndocrinologyEctopic calcification

Abstract

fetched live from OpenAlex

BACKGROUND: Vascular calcification is common among patients undergoing dialysis and is associated with mortality. Factors such as osteoprotegerin (OPG), osteopontin (OPN), bone morphogenic protein-7 (BMP-7), and fetuin-A are involved in vascular calcification. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: OPG, OPN, BMP-7, and fetuin-A were measured in blood samples from 602 incident dialysis patients recruited from United States dialysis centers between 1995 and 1998 as part of the Choices for Healthy Outcomes In Caring for ESRD Study. Their association with all-cause and cardiovascular mortality were assessed using Cox proportional hazards models adjusted for demographic characteristics, comorbidity, serum phosphate, and calcium. An interaction with diabetes was tested because of its known association with vascular calcification. Predictive accuracy of selected biomarkers was explored by C-statistics in nested models with training and validation subcohorts. RESULTS: Higher OPG and lower fetuin-A levels were associated with higher mortality over up to 13 years of follow-up (median, 3.4 years). The adjusted hazard ratios (HR) for highest versus lowest tertile were 1.49 (95% confidence interval [95% CI], 1.08 to 2.06) for OPG and 0.69 (95% CI, 0.52 to 0.92) for fetuin-A. In stratified models, the highest tertile of OPG was associated with higher mortality among patients without diabetes (HR, 2.42; 95% CI, 1.35 to 4.34), but not patients with diabetes (HR, 1.26; 95% CI, 0.82 to 1.93; P for interaction=0.001). In terms of cardiovascular mortality, higher fetuin-A was associated with lower risk (HR, 0.85 per 0.1 g/L: 95% CI, 0.75 to 0.96). In patients without diabetes, higher OPG was associated with greater risk (HR for highest versus lowest tertile, 2.91; 95% CI, 1.06 to 7.99), but not in patients with diabetes or overall. OPN and BMP-7 were not independently associated with outcomes overall. The addition of OPG and fetuin-A did not significantly improve predictive accuracy of mortality. CONCLUSIONS: OPG and fetuin-A may be risk factors for all-cause and cardiovascular mortality in patients undergoing dialysis, but do not improve risk prediction.

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 score0.004
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.341
Teacher spread0.317 · 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

Citations78
Published2014
Admission routes1
Has abstractyes

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