State-of-the-Art review: Hypertension practice guidelines in the era of COVID-19
Bibliographic record
Abstract
The global burden of hypertension (HTN) is immense and increasing. In fact, HTN is the leading risk factor for adverse cardiovascular disease outcomes. Due to the critical significance and increasing prevalence of the disease, several national and international societies have recently updated their guidelines for the diagnosis and treatment of HTN. In consideration of the COVID-19 pandemic, this report provides clinicians with the best strategies to prevent HTN, manage the acute and long-term cardiac complications of HTN, and provide the best evidence-based care to patients in an ever-changing healthcare environment. The overarching goal of the various HTN guidelines is to provide easily accessible information to healthcare providers and public health officials, which is key for optimal clinical practice. However, the COVID-19 pandemic has challenged the ability to provide safe care to the most vulnerable hypertensive populations throughout the world. Therefore, this review compares the most recent guidelines of the 2017 American College of Cardiology/American Heart Association and multiple U.S. societies, the 2018 European Society of Cardiology/European Society of Hypertension, the 2019 National Institute for Care and Health Excellence, and the 2020 International Society of Hypertension. While a partial emphasis is placed on the management of HTN in the midst of COVID-19, this review will summarize current concepts and emerging data from the listed HTN guidelines on the diagnosis, monitoring, management, and evidence-based treatments in adults.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".