Anaemia, Iron Deficiency and Heart Failure in 2020: Facts and Numbers
Why this work is in the frame
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Bibliographic record
Abstract
Anaemia is defined by WHO as Hb < 13.0 g/dL in male adults and <12.0 g/dL in female adults. It is a common comorbidity in patients of heart failure with both HFrEF and HFpEF. The incidence ranges between 30% and 50%, though in certain communities, it is likely to be higher still. Elderly age, severe heart failure, poor nutrition, and elevation of inflammatory markers are associated with a higher incidence of anaemia. However, the commonest contributing factor to anaemia in HF is iron deficiency. In a Canadian study of 12 065 patients, the incidence of absolute ID was 21% in anaemic patients. Many other western studies have also quoted incidences varying between 35% and 43%. The earlier attempts to improve outcomes by supplementation with Erythropoietic-stimulating factors were unsuccessful and resulted in a higher incidence of thrombotic events. Iron deficiency (ID) has emerged as an important factor in patients of HF, even in those without anaemia and worsens outcomes. It is defined as Ferritin levels below 100 mcg/L or 100-299 μg/L with transferrin saturation of <20%. Attempts to correct ID by oral supplementation have been unsuccessful as seen in IRON-HF and IRONOUT-HF trials. FAIR-HF and CONFIRM-HF conclusively established the role of IV Iron in improving exercise capacity and quality of life in patients with HFrEF. ESC guidelines have given a class IC indication for testing all heart failure patients for ID, and an IIaA recommendation for its correction by IV ferric carboxymaltose was found to be deficient. Ongoing trials will establish the role of IV iron in improving mortality and in HFpEF patients and in patients with acute heart failure.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it