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Record W1976299512 · doi:10.4061/2011/351291

Heart-Kidney Interaction: Epidemiology of Cardiorenal Syndromes

2010· article· en· W1976299512 on OpenAlex
Dinna N. Cruz, Sean M. Bagshaw

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

VenueInternational Journal of Nephrology · 2010
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Alberta Hospital
FundersFondation pour la Recherche MédicaleInternational Society of Nephrology
KeywordsCardiorenal syndromeEpidemiologyMedicineIntensive care medicineKidney diseaseDialysisDiseaseIdentification (biology)PathologyInternal medicine

Abstract

fetched live from OpenAlex

Cardiac and kidney diseases are common, increasingly encountered, and often coexist. Recently, the Acute Dialysis Quality Initiative (ADQI) Working Group convened a consensus conference to develop a classification scheme for the CRS and for five discrete subtypes. These CRS subtypes likely share pathophysiologic mechanisms, however, also have distinguishing clinical features, in terms of precipitating events, risk identification, natural history, and outcomes. Knowledge of the epidemiology of heart-kidney interaction stratified by the proposed CRS subtypes is increasingly important for understanding the overall burden of disease for each CRS subtype, along with associated morbidity, mortality, and health resource utilization. Likewise, an understanding of the epidemiology of CRS is necessary for characterizing whether there exists important knowledge gaps and to aid in the design of clinical studies. This paper will provide a summary of the epidemiology of the cardiorenal syndrome and its subtypes.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.029
GPT teacher head0.349
Teacher spread0.320 · 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