Can sphygmomanometers designed for self-measurement of blood pressure in the home be used in office practice?
Bibliographic record
Abstract
OBJECTIVE: To examine the possibility of measuring automated office (AO) blood pressure (BP) using home BP recorders in place of the more expensive, fully automated devices designed specifically for professional use. PARTICIPANTS AND METHODS: Three groups of 100 patients each had five AOBP readings recorded with one of three home BP devices while resting alone in a quiet examining room. These devices were also used to obtain 24 home BP readings during 6 days. Five AOBP readings were also recorded using the BpTRU device and all patients had 24-h ambulatory BP and manual BP readings taken. RESULTS: Mean systolic AOBP was within 3-4 mmHg of the mean awake ambulatory BP for each of the three home BP recorders whether used in the office setting or at home. Diastolic readings tended to be higher than the corresponding awake ambulatory BP. For the 139 patients with hypertension, mean (±standard deviation) AOBP taken with the home BP devices (146±14/86±12) was higher (P<0.001) than the awake ambulatory BP (142±11/81±12) and AOBP taken with the BpTRU device (141±15/82±12). Systolic BP at home (142±14/85±10) was also similar to the awake ambulatory BP but diastolic BP was higher (P<0.001). There were no significant differences in correlation coefficients between each set of AOBP readings and awake ambulatory BP. CONCLUSION: Home BP devices may be used to record AOBP in selected patients. However, a fully automated sphygmomanometer is still the device of choice for obtaining AOBP readings similar to the awake ambulatory BP in patients with suspected hypertension.
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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 | 0.012 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".