Bibliographic record
Abstract
This issue of the journal includes the new recommendations on management of acute heart failure (HF).1 This is a joint effort of the Heart Failure Association of the European Society of Cardiology (ESC), the European Society of Emergency Medicine and the Society of Academic Emergency Medicine. Recommendations about prehospital and hospital management, personnel involved, medications and discharge criteria are given.1 In addition to natriuretic peptides, an armamentarium of new biomarkers related with myocardial cell loss, fibrosis, infection and renal function have been introduced for HF patients. A state of the art review about new biomarkers in HF shows the best emerging candidates for the clinical assessment and management of patients with HF.2 Four articles are related with the epidemiology of HF. A report from the EURObservational Research Programme Pilot survey on Atrial Fibrillation shows the characteristics and prognosis of the patients with atrial fibrillation and HF. An analysis of the national trends in rate of patients hospitalized for HF and HF mortality in France, 2000–2012 shows, using data from the French National Hospitalization and national Mortality Databases, a slight decrease in HF hospitalizations in men, but not in women, and an overall decrease in HF mortality by 3.3% per year, larger, also in this case, in men than in women. Despite this improvement, HF remains a leading cause of hospitalizations and death.3 ExtraHF, the first European survey on implementation of exercise training in HF patients shows that an exercise training program is still not implemented by 67/170 (40%) cardiac centers with lack of resources as the most important cause.4 Marked geographical differences among patients hospitalized for HF in the Aliskiren Trial on Acute Heart Failure Outcomes (ASTRONAUT) are shown with annual mortality rates ranging from 7.3% in North America to 26.7% in Asia/Pacific, a difference largely driven by sudden cardiac death, and annual HF hospitalizations rates ranging from 22.7% in Latin America to 43.9% in North America.5 Dementia is probably one of the most important and of the least studied comorbidities of HF. In this issue of the journal, the different types of HF, mostly HF with preserved ejection fraction, and of dementia, mostly vascular, and the lack of prognostic impact of dementia are shown by an analysis of linked Swedish HF and dementia registries.6 Stewart et al. present a nurse-led secondary prevention programme to prevent HF in high-risk individuals. This study was a pragmatic, single-centre, open-label, randomized controlled trial with blinded endpoint adjudication performed in 624 cardiac inpatients at risk for development of HF.7 During 51 ± 8 months of follow-up, there were no differences in the rate of de novo HF hospitalization or all-cause mortality (primary endpoint) with, however, fewer days of hospitalization and a better cardiac recovery on echocardiography at 3 years in the intervention group.7
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.481 | 0.436 |
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".