Virtual management of hypertension: lessons from the COVID-19 pandemic–International Society of Hypertension position paper endorsed by the World Hypertension League and European Society of Hypertension
Bibliographic record
Abstract
The coronavirus disease 2019 pandemic caused an unprecedented shift from in person care to delivering healthcare remotely. To limit infectious spread, patients and providers rapidly adopted distant evaluation with online or telephone-based diagnosis and management of hypertension. It is likely that virtual care of chronic diseases including hypertension will continue in some form into the future. The purpose of the International Society of Hypertension's (ISH) position paper is to provide practical guidance on the virtual management of hypertension to improve its diagnosis and blood pressure control based on the currently available evidence and international experts' opinion for nonpregnant adults. Virtual care represents the provision of healthcare services at a distance with communication conducted between healthcare providers, healthcare users and their circle of care. This statement provides consensus guidance on: selecting blood pressure monitoring devices, accurate home blood pressure assessments, delivering patient education virtually, health behavior modification, medication adjustment and long-term virtual monitoring. We further provide recommendations on modalities for the virtual assessment and management of hypertension across the spectrum of resource availability and patient ability.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".