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Record W2094781356 · doi:10.1159/000170173

Clinical Aspects of Cardiomyopathy in Dialysis Patients

2008· article· en· W2094781356 on OpenAlexaff
Patrick S. Parfrey, John D. Harriett

Bibliographic record

VenueBlood Purification · 2008
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. John’s Health Sciences Centre
Fundersnot available
KeywordsMedicineLeft ventricular hypertrophyHeart failureInternal medicineCardiologyAnemiaUremiaDialysisHypoalbuminemiaDiabetes mellitusKidney diseaseCardiomyopathyBlood pressureEndocrinology

Abstract

fetched live from OpenAlex

The burden of cardiac disease in dialysis patients is high. Congestive heart failure, ischemic heart disease, left ventricular hypertrophy, and systolic dysfunction occur frequently and are associated with an adverse prognosis. In addition, during dialysis therapy anemia, hypoalbuminemia, low blood pressure, and lower serum creatinine levels are adverse predictors of mortality. Risk factors for systolic dysfunction include older age, ischemic heart disease, hyperparathyroidism, and smoking. Risk factors for left ventricular hypertrophy include older age, hypertension, anemia, and diabetes mellitus. Interventions with potential for improving cardiomyopathy include normalization of hematocrit with erythropoietin, improved uremia therapy, and angiotensin-converting enzyme inhibitors. Trials to determine the most appropriate interventions to reduce the impact of cardiac disease in chronic uremia are urgently required.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.278
Teacher spread0.255 · 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 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

Citations21
Published2008
Admission routes1
Has abstractyes

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