Optimum frequency of office blood pressure measurement using an automated sphygmomanometer
Bibliographic record
Abstract
OBJECTIVE: To determine the optimum interval between serial blood pressure measurements using an automated BpTRU sphygmomanometer. METHODS: Two groups of 200 patients each had automated office measurements taken using the BpTRU device at either 1-min or 2-min intervals from the start of one reading to the start of the next reading with a 24-h ambulatory blood pressure (ABP) recording being performed. Another series of 50 patients had BpTRU readings taken at 1-min and 2-min intervals before and after 24-h ABP monitoring. The difference between the mean awake ABP and the mean automated office BP readings were compared for recordings taken at 1-min versus 2-min intervals. RESULTS: In the between-patient comparison (n=400), mean awake ABP was similar to automated BP recordings in the examining room at either 1-min or 2-min intervals except for a slightly lower (-4 mmHg) diastolic BP with the 1-min interval (P<0.01 vs. ABP). In the within-patient comparison (n=50), there was no consistent difference between automated BP readings taken in the examining room at 1-min versus 2-min intervals. Overall, the mean automated BP values tended to be slightly lower than the mean awake ABP. CONCLUSION: Automated measurement of BP in the office setting with devices such as the BpTRU can be taken as frequently as every 1 min without affecting the accuracy of the reading. Small differences in BP between the 1 and 2-min settings and between the automated BpTRU and ABP readings were within accepted clinical standards for validation criteria.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".