Automated office blood pressure measurement in primary care.
Bibliographic record
Abstract
OBJECTIVE: To provide FPs with detailed knowledge of automated office blood pressure (AOBP) measurement, its potential role in primary care, and its proper use in the diagnosis and management of hypertension. SOURCES OF INFORMATION: Comprehensive monitoring and collection of scientific articles on AOBP by the authors since its introduction. MAIN MESSAGE: Automated office blood pressure measurement maintains a role for blood pressure (BP) readings taken in the office setting. Clinical research studies have reported a substantially stronger relationship between awake ambulatory BP measurement and AOBP measurement compared with manual BP recorded during routine visits to the patient's physician. Automated office blood pressure measurement produces mean BP values comparable to awake ambulatory BP and home BP values. Compared with routine manual office BP measurement, AOBP correlates more strongly with awake ambulatory BP measurement, shows less digit preference, is more consistent from visit to visit, is similar both within and outside of the physician's office, virtually eliminates office-induced hypertension, and is associated with less masked hypertension. It is estimated that more than 25% of Canadian primary care physicians are now using AOBP measurement in their office practices. The use of AOBP to diagnose hypertension has been recommended by the Canadian Hypertension Education Program since 2010. CONCLUSION: There is now sufficient evidence to incorporate AOBP measurement into primary care as an alternative to manual BP measurement.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".