Bibliographic record
Abstract
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.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.075 | 0.066 |
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".