Notice bibliographique
Résumé
Important reviews regard the role of microRNAs, diagnosis and management of aortic stenosis in patients with heart failure (HF) and the use of composite endpoints in HF trials. Many research articles regard epidemiology. Thirty-year trends in HF hospitalizations, mortality and co-morbidities were analysed in a Danish nationwide cohort of 317 161 patients with first-time HF hospitalizations in the years 1983–2012. The annual rate of HF hospitalizations increased slightly up to 2000 and decreased markedly, 3.5% per year, thereafter. Similarly, between the years 1983–87 and 2008–12, the 1-year and the 1 to 5-year mortality rates declined from 45% to 33% and from 59% to 43%, respectively. The co-morbidity burden has increased over time with a larger role as a mortality cause.1 Conversely, no improvement in survival was found between 2003 and 2012 in 5908 patients with HF and reduced ejection fraction (HFrEF) enrolled in the Swedish HF Registry. This was related to a high but stable use of renin-angiotensin system antagonists and beta-blockers, variable use of mineralocorticoid antagonists and with a low absolute use of CRT and ICD, implanted in <10% of the patients in most of the hospitals. No implementation of evidence-based therapies occurred in this decade.2 Komajda et al. examined adherence to guideline-recommended drugs for HF in a large international, longitudinal survey of 7092 patients with HFrEF and a recent HF hospitalization. Adherence was good in 67% of the patients and moderate in other 25%. However, target doses and ≥50% target doses were administered in a very low proportion of patients.3 Van Hagen et al. have used the worldwide Registry Of Pregnancy And Cardiac disease (ROPAC), including 2742 pregnant women with cardiac disease, to validate the modified World Health Organization (mWHO) risk classification. Congenital heart disease was the most prevalent disease in advanced countries and valvular heart disease was more common in emerging countries. Cardiac events occurred in 12.8% of the patients in advanced countries vs. 36.3% in emerging countries, and the mWHO risk score performed better in advanced countries. Signs of HF and atrial fibrillation had an independent additive prognostic value.4 Abdominal obesity, measured as waist circumference and the waist-to-hip ratio, but not general obesity, assessed by the body mass index, was related to subclinical systolic dysfunction, measured by the global longitudinal strain, in 729 subjects from the population-based, prospective Cardiovascular Abnormalities and Brain Lesions study.5 These data add to previous studies regarding the determinants of subclinical cardiac dysfunction6 and the role of obesity in HF.7 With respect to prognosis, an analysis of the Euro HF Survey-1 shows that the diagnostic coding position is associated with outcomes in patients with acute HF. In-hospital mortality was 16% in patients with a secondary diagnosis of HF vs. 7% in those with a primary diagnosis and 4% in those where the HF contribution was uncertain.8 A novel parameter of right ventricular function, measured as the product of TAPSE and the transtricuspid pressure gradient, had an independent prognostic value in patients with acute HF.9
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».