How Often Do Office Blood Pressure Measurements Fail to Identify True Hypertension?: An Exploration of White-Coat Normotension
Bibliographic record
Abstract
BACKGROUND: The often-observed differences between ambulatory (ABP) and office blood pressure (OBP) measurements have brought attention to the problem of misdiagnoses. Although there has been much focus on white-coat hypertension (elevated OBP with normal ABP means), few studies have examined "white-coat normotension" (WCN; normal OBP with elevated ABP means). OBJECTIVES: To describe patients with WCN in terms of prevalence and quantitative differences between ABP and OBP; to identify psychological and demographic features that discriminate them from true normotensive patients; and to offer possible corrections for diagnostic limitations of OBP measurements in clinical practice. DESIGN AND METHODS: Five OBP measurements and 10- to 12-hour daytime ABP monitoring in 319 presumed healthy participants. RESULTS: Prevalence rates of WCN were 23% for systolic BP and 24% for diastolic BP. Participants with WCN were more often male, past smokers, and older and consumed more alcohol. Increasing the number of office readings and discarding the first office reading did not improve the accuracy of OBP measurements. Participants with BP of 10 mm Hg above or below the 140/90 office reading cutoff showed the lowest accuracy, with more than 50% of normotensive diagnoses being incorrect. CONCLUSIONS: Office measures of BP lack sensitivity, missing a sizable portion of individuals who have hypertensive mean ABP measurements. Subjects with WCN differ from true normotensive subjects on several demographic and lifestyle variables. Only those office readings averaging 20 points above or below the 140/90 cutoff represent safe diagnostic information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".