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Record W2170847019 · doi:10.1586/14779072.6.2.199

Recognition and treatment of anemia in the setting of heart failure due to systolic left ventricular dysfunction

2008· review· en· W2170847019 on OpenAlexaff
Jonathan G. Howlett

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2008
Typereview
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsNova Scotia HospitalDalhousie University
Fundersnot available
KeywordsMedicineHeart failureAnemiaDarbepoetin alfaErythropoietinInternal medicineCardiologyIntensive care medicineRandomized controlled trial

Abstract

fetched live from OpenAlex

Anemia is increasingly recognized as a common, important and treatable condition in patients with congestive heart failure. Despite increasing knowledge of anemia, as well as its co-association with chronic renal disease, advanced New York Heart Association class and worse prognosis, there are very few evidence-based recommendations for treatment. The use of supplemental iron, especially intravenous forms, for the treatment of iron-deficiency anemia in heart failure patients is associated with improved symptoms, cardiac size and function, and possibly improved outcomes. However, many patients with heart failure suffer from anemia due to other causes, including renal failure (so-called cardiorenal syndrome), erythropoietin resistance, possible ACE inhibitor use and extracellular fluid expansion. The association between anemia and adequate iron stores has led to interest in the use of erythrocyte-stimulating agents, such as erythropoietin and darbepoetin. While early data are promising, recent evidence in non-heart failure trials has led to caution in their use and given way to anticipation of results of ongoing definitive randomized trials of this therapy, such as the Trial to Reduce Cardiovascular Events with Aranesp Therapy (TREAT) and Reduction of Events with Darbepoetin-alpha in Heart Failure (RED-HF) studies.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.003
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 head0.038
GPT teacher head0.316
Teacher spread0.278 · 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 designOther design
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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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