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Record W7128168251 · doi:10.1093/joneph/20.4.417

Warfarin and aortic valve calcification in hemodialysis patients

2007· article· en· W7128168251 on OpenAlexaff
R. M. Holden, Anthony Sanfilippo, Wilma M. Hopman, Deborah Zimmerman, Jocelyn Garland, A. Ross Morton

Bibliographic record

VenueJournal of Nephrology · 2007
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of OttawaKingston General HospitalQueen's University
Fundersnot available
KeywordsWarfarinCalcificationHemodialysisDialysisCalcinosisOdds ratioProspective cohort studyHazard ratio

Abstract

fetched live from OpenAlex

Abstract Background: This retrospective cohort study was designed to determine the association between long-term exposure to warfarin and severity of aortic valve (AV) calcification in hemodialysis (HD) patients. Methods: One hundred and eight HD patients underwent a study-specific echocardiogram. A grading scheme was used to classify AV calcification as none, mild, moderate and severe. Demographic, biochemical and medication data were abstracted by chart review. Results: One hundred and eight subjects were enrolled. A minority had no calcification (n=17, 15.7%), the majority had mild calcification (n=62, 57.4%), and fewer had calcification rated as moderate (n=16, 14.8%) or severe (n=13, 12%). Dialysis vintage was associated with severity of AV calcification (p=0.04). The 18 subjects with long-term warfarin exposure (36.7 ± 19.7 months) were more likely to have severe AV calcification (p=0.04). The odds ratio of falling into a higher category of AV calcification following 18 months of warfarin was 3.77 (95% confidence ratio, 0.97-14.70; p=0.055). There was an association between lifetime months of warfarin exposure and severity of AV calcification (p=0.004) that was independent of dialysis vintage, calcium and calcitriol intake. Conclusions: The data suggest that warfarin may be associated with severity of AV calcification in HD patients and support the need for prospective studies.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.295
Teacher spread0.281 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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