Notice bibliographique
Résumé
In the current issue of the American Journal of Hypertension for March 2021 we have a Compendium on hypertension across the life span, 2 original papers on COVID-19 and a commentary on one of them, and a manuscript on referrals to outpatient follow-up of hypertensive patients seen in the emergency department. The Compendium was Guest Edited by one of the Journal’s Associate Editors, Paul Muntner, who writes in his introduction to the Compendium1 that the prevalence of hypertension increases with age and the lifetime risk for hypertension exceeds 80% among US adults. He then describes the different aspects addressed by the Compendium, including blood pressure trajectories across the life course by Allen and Khan,2 blood pressure in childhood and adolescence by Hardy and Urbina,3 blood pressure in young adults and cardiovascular disease later in life by Yano,4 blood pressure control among older adults with hypertension and introduction of a framework for improving care by Bowling et al.,5 and finally an article on DNA methylation and blood pressure phenotypes by Irvin et al.6 Original articles in this issue include a brief communication by Rieder et al. on ACE-2, angiotensin II, and aldosterone levels in patients with COVID-19,7 which is accompanied by a commentary by Wenzel and Kintscher.8 Rieder et al. report on a prospective single-center study in which they determined the serum levels of ACE-2, angiotensin II, and aldosterone in patients with COVID-19 compared to control patients presenting with similar symptoms in the emergency department.7 They did not find that any of the components of the renin–angiotensin–aldosterone system that they measured was altered in patients sick with COVID-19. Wenzel and Kintscher8 comment that these data need to be confirmed in larger cohorts including cases with more severe forms of COVID-19, but they suggest that a SARS-CoV-2 infection does not result in major changes of the renin-angiotensin-aldosterone system, and specifically that soluble ACE2 is not altered. They also speculate on soluble ACE2 as a therapeutic target. Also in relation to COVID-19, Caillon et al. describe in this issue, based on a cohort of COVID-19 patients from Wuhan, China, models with parameters recorded on arrival to the emergency department, that predict outcome of these patients.10 Interestingly, from 43 variables they derived a model that predicts death with 13 variables, and a Cox regression model with 7 of the 13 that predicts probability of survival. Importantly, systolic blood pressure on arrival, but not history of hypertension was a covariate in the mortality and survival prediction models. The authors conclude that these models could contribute to evidence-based risk prediction and decision-making at hospital triage. This could ensure providing the most appropriate care and could contribute to improved patient outcomes. In a final original manuscript in this issue of the Journal, Giaimo et al. report a study of 40 patients suffering from a hypertensive urgency referred from the emergency department to outpatient hypertension management.9 They demonstrate in this pilot study that referral from the emergency department to primary care provides safe, timely care for these high cardiovascular risk patients and importantly is associated with a reduction in the patients’ blood pressure, and also diminished utilization and congestion of the emergency department. The author declared no conflict of interest.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,017 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,066 |
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 source (Gemma direct ou Codex distillé), 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 ».