June 2021 at a glance: focus on epidemiology, biomarkers and medical treatment
Bibliographic record
Abstract
The epidemiology of heart failure (HF) is still unsettled, above all in European countries, because of marked differences in coding and management modalities.To date, most of our data came from analyses of dedicated studies and trials.1,2 The Heart Failure Association (HFA) Atlas is a novel European data set, developed to provide a contemporary description of HF epidemiology and management.3,4 The median incidence of HF was 3.2 [interquartile range (IQR) 2.66-4.17]cases per 1000 person-years; the median HF prevalence was 17.20 (IQR 14.30-21) cases per 1000 people and the median number of HF hospitalizations was 2671 (IQR 1771-4317) per million people, annually.A large heterogeneity among different countries both with respect to epidemiology and management was shown.3 Medical treatment Phenotype-based therapiesEvidence-based medical treatment is still underused in patients with HF and reduced ejection fraction.23,24 In a HFA consensus document, Rosano et al. 25 identified nine patient profiles which may inform treatment choices.These profiles include patients with atrial fibrillation, low blood pressure, tachycardia, chronic kidney disease, hyperkalaemia, congestion, or pre-discharge status.A strategy of rapid sequencing of evidence-based treatment was also proposed to optimize medical treatment.26
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.117 | 0.076 |
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".