Anaemia in chronic heart failure: what is its frequency in the UK and its underlying causes?
Bibliographic record
Abstract
In two recent studies from Israel, Silverberg and colleagues noted that anaemia was common in chronic heart failure (CHF).1,2 Moreover, treatment with combined erythropoietin and intravenous ferrous sulfate not only increased haemoglobin concentrations but, more importantly, was associated with improvements in cardiac function, New York Heart Association (NYHA) functional class, renal function, and falls in the need for diuretics and hospitalisation. The importance of anaemia in CHF was recently highlighted by data from the SOLVD study where anaemia was found to be a risk factor for mortality.3 Two questions now arise. Firstly, how common is anaemia in CHF patients in the UK? Secondly, what causes this anaemia in CHF? This second question is pertinent because there are numerous possible causes of anaemia in such patients. For example, aspirin use is widespread in CHF patients, raising the possibility of iron deficiency anaemia. Renal dysfunction is also common, raising the possibility of an anaemia of chronic disorder. Since CHF patients are elderly, coincidental hypothyroidism or pernicious anaemia could also contribute to the anaemia. We therefore set out to assess these two questions retrospectively. After all, erythropoietin would not be an appropriate treatment in CHF anaemia where iron deficiency …
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".