Can blood pressure measurements taken in the physician’s office avoid the ‘white coat’ bias?
Bibliographic record
Abstract
OBJECTIVE: Obtaining an accurate blood pressure (BP) reading is vital for diagnosing hypertension. However, BP measures taken in the physician's clinic (CBP) are subject to the 'white coat' bias. Measurements taken outside the office using ambulatory (ABP) and home (HBP) monitoring are superior predictors of cardiovascular diseases compared with CBP, but ABP remains underutilized because of the effort and expense involved. Unfortunately, HBP has limitations, including questionable device validity and patient compliance. Thus, it is important to identify feasible alternative techniques to measure BP in the office that will increase the accuracy of the diagnosis. METHODS: Auscultatory BP was measured in 249 patients in a nonclinical setting by trained technicians (NCBP); on the following day, patients were taken to their physician (CBP). They were also given an HBP monitor, and a 36 h ABP monitoring. Because ABP is considered the gold standard for prediction of cardiovascular disease, these readings were used as the criterion in a statistical model in which CBP, HBP, and NCBP were entered as predictors. The level of agreement between measurements was estimated. RESULTS: Multiple regression analysis showed that HBP and NCBP (P < 0.001) explained 94 and 87% of the variance in systolic and diastolic ABP, respectively. The agreement between NCBP and ABP was greater than that between CBP and ABP or between HBP. CONCLUSION: When ABP monitoring and HBP monitoring are not options, the NCBP at the clinic can avoid the white coat bias and therefore improve diagnosis.
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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 | 0.035 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".