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Record W2333669312 · doi:10.1097/mnh.0000000000000213

Cardiovascular disease in chronic kidney disease in 2015

2016· review· en· W2333669312 on OpenAlexaff
Maneesh Sud, David Naimark

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2016
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineKidney diseaseCoronary artery diseaseIntensive care medicineDiseaseInternal medicineCardiologyHeart failureRevascularizationCause of deathEnd stage renal diseaseMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Chronic kidney disease (CKD) is strongly linked to premature cardiovascular disease, which is the leading cause of death before end-stage renal disease in these patients. Herein, we review recent literature from 2014 to 2015 that has advanced our understanding of cardiovascular outcomes in patients with advanced and/or progressive CKD. RECENT FINDINGS: We focus on new data describing the mechanisms of cardiac death in patients with CKD as well as the novel associations between cardiac events and the competing risks of end-stage renal disease and pre-end-stage renal disease death. We review new controversies in multivessel revascularization of complex coronary artery disease in CKD and, finally, the treatment of systolic heart failure in advanced CKD, including the use of implantable defibrillator therapy. SUMMARY: Marked advances in the understanding of cardiovascular disease in CKD have occurred in the past year, namely from retrospective and registry data. Although exciting, these recent studies highlight the urgent need for randomized control trials to guide therapeutic decisions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.371
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations22
Published2016
Admission routes1
Has abstractyes

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