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CHRONIC KIDNEY DISEASE AND ARRHYTHMIAS: CONCLUSIONS FROM A KIDNEY DISEASE: IMPROVING GLOBAL OUTCOMES (KDIGO) CONTROVERSIES CONFERENCE

2019· article· en· W2915087414 on OpenAlexaff
Peter J. Blankestijn, Juan Jesús Carrero, Catherine M. Clase, Rajat Deo, Charles A. Herzog, Scott E. Kasner, Rod Passman, Roberto Pecoits‐Filho, Holger Reinecke, Gautam R. Shrof, Wojciech Zaręba, Michael Cheung, David C. Wheeler, Wolfgang C. Winkelmayer­, Christoph Wanner, Mintu P. Turakhia

Bibliographic record

VenueNephrology (Saint-Petersburg) · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpact
Fundersnot available
KeywordsMedicineKidney diseaseAtrial fibrillationAtrial flutterIntensive care medicineInternal medicineCardiologySupraventricular arrhythmiaDiseaseHeart failureHeart diseaseRandomized controlled trial

Abstract

fetched live from OpenAlex

Patients with chronic kidney disease (CKD) are predisposed to heart rhythm disorders, including atrial fibrillation (AF)/atrial flutter, supraventricular tachycardias, ventricular arrhythmias, and sudden cardiac death (SCD). While treatment options, including drug, device, and procedural therapies, are available, their use in the setting of CKD is complex and limited. Patients with CKD and end-stage kidney disease have historically been under-represented or excluded from randomized trials of arrhythmia treatment strategies, 1 although this situation is changing. Cardiovascular society consensus documents have recently identified evidence gaps for treating patients with CKD and heart rhythm disorders. To identify key issues relevant to the optimal prevention, management, and treatment of arrhythmias and their complications in patients with kidney disease, Kidney Disease: Improving Global Outcomes (KDIGO) convened an international, multidisciplinary Controversies Conference in Berlin, Germany, titled CKD and Arrhythmias in October 2016.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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.

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

Citations12
Published2019
Admission routes1
Has abstractyes

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