MétaCan
Menu
Back to cohort
Record W2974932310 · doi:10.1016/j.jacc.2019.08.1017

Chronic Kidney Disease and Coronary Artery Disease

2019· review· en· W2974932310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American College of Cardiology · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersAbbott VascularAkebia TherapeuticsBritish Heart FoundationFresenius Medical Care North AmericaBoston Scientific CorporationEdwards LifesciencesDaiichi-SankyoPfizerAmgen
KeywordsMedicineKidney diseaseCoronary artery diseaseDiabetes mellitusDiseaseInternal medicineRisk factorIntensive care medicineUremiaEndocrinology

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a major risk factor for coronary artery disease (CAD). As well as their high prevalence of traditional CAD risk factors, such as diabetes and hypertension, persons with CKD are also exposed to other nontraditional, uremia-related cardiovascular disease risk factors, including inflammation, oxidative stress, and abnormal calcium-phosphorus metabolism. CKD and end-stage kidney disease not only increase the risk of CAD, but they also modify its clinical presentation and cardinal symptoms. Management of CAD is complicated in CKD patients, due to their likelihood of comorbid conditions and potential for side effects during interventions. This summary of the Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference on CAD and CKD (including end-stage kidney disease and transplant recipients) seeks to improve understanding of the epidemiology, pathophysiology, diagnosis, and treatment of CAD in CKD and to identify knowledge gaps, areas of controversy, and priorities for research.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.634
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.300
Teacher spread0.279 · 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