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Record W2123275154 · doi:10.1093/ndt/gfq252

ADQI 7: the clinical management of the Cardio-Renal syndromes: work group statements from the 7th ADQI consensus conference

2010· article· en· W2123275154 on OpenAlexaff
Andrew Davenport, Stefan D. Anker, Alexandre Mebazaa, Alberto Palazzuoli, Giorgio Vescovo, Rinaldo Bellomo, Piotr Ponikowski, Inder S. Anand, Nadia Aspromonte, Sean M. Bagshaw, Tomás Berl, Ilona Bobek, D. N. Cruz, Luciano Daliento, Mikko Haapio, Hans L. Hillege, Andrew A. House, N. Katz, Alan S. Maisel, Sunil Mankad, Peter A. McCullough, Federico Ronco, Andrew Shaw, G. Sheinfeld, Sachin Soni, Nereo Zamperetti, P. Zanco, Claudio Ronco

Bibliographic record

VenueNephrology Dialysis Transplantation · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsWestern UniversityUniversity of Alberta
Fundersnot available
KeywordsMedicineHeart failureIntensive care medicineKidney diseaseCardiorenal syndromeChronic renal failureInternal medicineRenal functionCardiologyDiseaseClinical trial

Abstract

fetched live from OpenAlex

Many patients with heart failure have underlying renal dysfunction, and similarly, patients with kidney failure are prone to cardiac failure. This has led to the concept of cardio-renal syndromes, which can be an acute or chronic cardio-renal syndrome, when cardiac failure causes deterioration in renal function, or acute and/or chronic Reno-Cardiac syndrome, when renal dysfunction leads to cardiac failure. Patients who develop these syndromes have increased risk of hospital admission and mortality. Although there are clinical guidelines for managing both heart failure and chronic kidney disease, there are no agreed guidelines for managing patients with cardio-renal and/or Reno-Cardiac syndromes, as these patients have typically been excluded from clinical trials. We have therefore reviewed the currently available published literature to outline a consensus of current best clinical practice for these patients.

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 score0.187
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.037
GPT teacher head0.325
Teacher spread0.288 · 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

Citations44
Published2010
Admission routes1
Has abstractyes

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