Recognition and treatment of anemia in the setting of heart failure due to systolic left ventricular dysfunction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".