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Record W2054685617 · doi:10.4061/2011/351672

Improving Prognosis Estimation in Patients with Heart Failure and the Cardiorenal Syndrome

2011· article· en· W2054685617 on OpenAlex
Husam Abdel‐Qadir, Shaan Chugh, Douglas S. Lee

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 · 2011
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsInstitute for Clinical Evaluative SciencesToronto General HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCardiorenal syndromeMedicineHeart failureMicroalbuminuriaIntensive care medicineRenal functionInternal medicineCardiology

Abstract

fetched live from OpenAlex

The coexistence of heart failure and renal dysfunction constitutes the "cardiorenal syndrome" which is increasingly recognized as a marker of poor prognosis. Patients with cardiorenal dysfunction constitute a large and heterogeneous group where individuals can have markedly different outcomes and disease courses. Thus, the determination of prognosis in this high risk group of patients may pose challenges for clinicians and for researchers alike. In this paper, we discuss the cardiorenal syndrome as it pertains to the patient with heart failure and considerations for further refining prognosis and outcomes in patients with heart failure and renal dysfunction. Conventional assessments of left ventricular function, renal clearance, and functional status can be complemented with identification of coexistent comorbidities, medication needs, microalbuminuria, anemia, biomarker levels, and pulmonary pressures to derive additional prognostic data that can aid management and provide future research directions for this challenging patient group.

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)0.000
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 score0.009
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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