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Anaemia, cardiovascular disease and kidney disease: integrating new knowledge in 2002

2003· review· en· W2333870310 on OpenAlexaff
Lesley A. Stevens, Adeera Levin

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2003
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKidney diseaseDiseaseContext (archaeology)MedicineIntensive care medicineObservational studyClinical trialDialysisBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The present review assesses recent publications, from 2001 until the present, which address the relationship between cardiovascular disease (CVD) and anaemia in patients with chronic kidney disease. RECENT FINDINGS: Insights from the recently published basic science literature have helped to place findings from clinical studies into a new context, and thereby assist us to understand and further explore the complex relationship between haemoglobin level and survival in chronic kidney disease. The effects of erythropoietin molecules and the presence of receptors in vascular endothelium, myocardium and other tissues are described. Both observational and interventional clinical studies are examined, and limitations in the methodology and statistical analysis of clinical studies are emphasized, but are given context within the body of literature preceding the past year's publications. SUMMARY: Data suggest that development of CVD in patients with kidney disease is multifactorial. Several factors associated with CVD are also associated with anaemia, thereby making causal arguments for the role of anaemia in CVD and survival difficult. Arguments are made for the importance of prevention of anaemia and of individualizing therapeutic goals for its treatment. Well designed prospective studies with both CVD events and mortality as outcomes, and with enrollment beginning before the start of dialysis, are essential if we are to determine the optimal therapeutic strategies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.355
Teacher spread0.263 · 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.

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

Citations18
Published2003
Admission routes1
Has abstractyes

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