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Record W2146130667 · doi:10.2215/cjn.02920313

Cardiorenal Syndrome

2013· review· en· W2146130667 on OpenAlexaff
Andrew A. House

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2013
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCardiorenal syndromeInternal medicineIntensive care medicineHeart failureCardiology

Abstract

fetched live from OpenAlex

Cardiorenal syndromes (CRSs) with bidirectional heart-kidney signaling are increasingly being recognized for their association with increased morbidity and mortality. In acute CRS, recognition of the importance of worsening kidney function complicating management of acute decompensated heart failure has led to the examination of this specific outcome in the context of acute heart failure clinical trials. In particular, the role of fluid overload and venous congestion has focused interest in the most effective use of diuretic therapy to relieve symptoms of heart failure while at the same time preserving kidney function. Additionally, many novel vasoactive therapies have been studied in recent years with the hopes of augmenting cardiac function, improving symptoms and patient outcomes, while maintaining or improving kidney function. Similarly, recent advances in our understanding of the pathophysiology of chronic CRS have led to reanalysis of kidney outcomes in pivotal trials in chronic congestive heart failure, and newer trials are including changes in kidney function as well as kidney injury biomarkers as prospectively monitored and adjudicated outcomes. This paper provides an overview of some new developments in the pharmacologic management of acute and chronic CRS, examines several reports that illustrate a key management principle for each subtype, and discusses opportunities for future 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.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.105
GPT teacher head0.424
Teacher spread0.319 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of NephrologySame topicHeart Failure Treatment and ManagementFrench-language works237,207