Are Automated Office Blood Pressure Readings More Variable Than Home Readings?
Bibliographic record
Abstract
A recent report from the American Heart Association stated that automated office blood pressure (AOBP) is preferred for evaluating office blood pressure (BP) because it is more accurate and devoid of white coat effect, which is mostly caused by higher systolic BP readings. However, AOBP has been criticized for being too variable to be used for identifying patients with possible hypertension. We, therefore, compared AOBP with home BP monitoring (HBPM) with respect to variability as determined by their relationship with the gold standard for determining BP status, awake ambulatory BP (ABP). The main focus was on systolic BP. Data on AOBP, HBPM, and awake ABP were collected on 300 patients referred from the community for 24-hour ambulatory BP monitoring. The SD of the difference between mean systolic awake ABP (136.4±11.5) and AOBP (131.2±15.7) was 13.6 mm Hg compared with 13.1 for the SD of the difference ( P =0.52) between the systolic awake ABP and the HBPM (136.7±16.1). Coefficients of correlation were slightly lower for systolic awake ABP versus AOBP ( r =0.54) compared with HBPM ( r =0.60). Coefficients of variation for AOBP (12.0%) and HBPM (11.8%) and variances between AOBP and HBPM were similar. Of the 139 patients with hypertension as defined by a manual office systolic BP ≥140 mm Hg, variability in BP readings as determined by the SDs of the mean difference versus awake ABP were similar ( P =0.56) for AOBP (14.6) and HBPM (13.9). Overall, both systolic AOBP and HBPM exhibited a similar degree of variability as assessed by the various methods.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".