Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension
Bibliographic record
Abstract
Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.014 | 0.025 |
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".