Emergence of Home Blood Pressure-Guided Management of Hypertension Based on Global Evidence
Bibliographic record
Abstract
Recent guidelines for the management of hypertension from the 2018 European Society of Cardiology/European Society of Hypertension and the 2017 American College of Cardiology (ACC)/American Heart Association (AHA)1,2 have stressed the importance of out-of-office blood pressure (BP) measurement for hypertension management. There has been a similar emphasis on out-of-office BP monitoring for the management of hypertension in the United Kingdom, Canada, Japan, and other Asian countries.3–6 Ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM) are 2 well-validated approaches for measuring out-of-office BP. In the published literature, HBPM is a term that has commonly referred to the self-measurement of BP at home, although in some studies, HBPM has been used to describe a provider or research assistant measuring an individual’s BP in his/her home. ABPM and HBPM can identify white coat hypertension (diagnostic disagreement between office and out-of-office BP in untreated subjects) and white coat uncontrolled hypertension (diagnostic disagreement in treated subjects).1–8 Although ABPM has been the preferred method for out-of-office measurement, the 2017 ACC/AHA BP guideline considered HBPM to be a more practical approach in clinical practice than ABPM, particularly for individuals taking antihypertensive medication. The 2014 Japanese Society of Hypertension Guidelines for the Management of Hypertension proposed HBPM as the most effective and practical for guiding antihypertensive medication initiation and titration in clinical care, while waiting for intervention trials demonstrating better cardiovascular outcomes in patients managed based on out-of-office BP levels
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".