Accuracy—Limiting Factor of Home Blood Pressure Monitors?
Bibliographic record
Abstract
In this issue of the American Journal of Hypertension, Padwal et al. report data that is extremely relevant clinically.1 A significant number of automatic oscillometric blood pressure (BP) monitors, owned by Albertans with hypertension, are inaccurate. In particular, in this well-designed study, the difference between systolic or diastolic BP taken simultaneously by the patient’s own automated oscillometric home BP device and by a 2-observer auscultatory reference standard BP measurement (mercury sphygmomanometer) was more than 5, 10, and 15 mm Hg in 69%, 29%, and 7% cases, respectively.1 These data are truly concerning given the fact that guidelines on the diagnosis and management of hypertension issued by major national professional organizations such as Hypertension Canada, American Society of Hypertension, Heart Foundation of Australia, and National Institute for Health and Care Excellence all endorse home BP monitoring.2–5 Accordingly, data on home BP readings do play a significant role in the decision making process for management of hypertension in the individual patient. In agreement with these trends, the market for oscillometric automated home BP monitors has expanded dramatically over the last decade or so, reaching over 1 billion US dollars.6 It is expected to increase by 13% annually and reach about 2 billion US dollars in 2022, mostly related to aging population, the high prevalence of hypertension in the elderly, and last but not least, by the ongoing emphasis on home BP monitoring.6
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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 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".