Consistent relationship between automated office blood pressure recorded in different settings
Bibliographic record
Abstract
OBJECTIVE: Conventional office blood pressure (BP) readings are affected by various factors including the presence of an observer and the setting. This study was undertaken to assess the consistency of automated self-measurement of BP in the office during repeat visits and in different settings. Automated office BP readings were also compared with the mean awake ambulatory BP. METHODS: BP readings were obtained using an automated BpTRU sphygmomanometer during routine visits to a hypertension specialist before and after 24-h ambulatory BP monitoring (ABPM) was performed. A third automated BP reading was obtained during the visit to the ABPM unit. RESULTS: There were no significant differences among the three automated office BP readings, which were all similar to the mean awake ambulatory BP. A manual BP reading taken by the ABPM technician was significantly higher (P<0.001) than the mean awake ambulatory BP. There was good agreement among the three automated office BP readings (intraclass correlation coefficient for systolic/diastolic BP r = 0.896/0.873). CONCLUSION: Mean automated office BP readings are consistent from visit-to-visit regardless of the setting in which they are taken and they are similar to the mean awake ambulatory BP.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".