Bibliographic record
Abstract
This issue has the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) nurse curriculum.1 The role of nurses in the care of heart failure (HF) patients is more and more important.2, 3 This curriculum by the HFA sets the standards of the knowledge, skills and professional duties of HF nurses in the ESC countries. Co-morbidities have a major role in the clinical presentation, prognosis and treatment of the patients with HF.4-6 This issue is focused on them. Iron deficiency (ID) is a major determinant of the clinical course and prognosis of HF patients. Differently from other co-morbidities, its direct correction can improve their symptoms, quality of life and clinical course. In this issue, one article reviews the effects of iron on the skeletal muscles.7 Two meta-analyses summarize the results of randomized controlled trials of iron therapy in anaemic adult patients with no chronic kidney disease (CKD) and in patients with HF, respectively.8, 9 A research shows the independent prognostic value of ID in patients hospitalized for acute HF.10 The association of high serum erythropoietin levels and worse outcomes in patients with acute HF and, with respect to HF development, in subjects with albuminuria, is shown by two different studies in this issue of our journal.11, 12 Other articles regard CKD, liver dysfunction, chronic obstructive pulmonary disease, obesity and frailty. In a study, diabetes changed the prognostic value of obesity in the patients with HF. Differently from non-diabetics, diabetics did not benefit from the obesity paradox and their mortality was not related to their body mass index.13
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".