Use of automated office blood pressure measurement to reduce the white coat response
Bibliographic record
Abstract
OBJECTIVE: To examine the possibility of reducing the white coat response using an automated sphygmomanometer designed for office use, the BpTRU. Consecutive patients referred from physicians in the community to an ambulatory blood pressure (ABP) monitoring unit in an academic hospital were included in the study. PARTICIPANTS AND METHODS: A total of 309 patients referred for diagnosis or management of hypertension were studied. Differences between mean awake ABP and BP readings taken by the patient's own physician using a manual sphygmomanometer or the automated BpTRU device with the patient resting alone in the ABP monitoring unit were compared. RESULTS: BP recorded in the examining room using an automated device (132 +/- 19/75 +/- 12) was similar to the mean awake ABP (134 +/- 12/77 +/- 10) with both values being lower (P < 0.001) than the BP recorded on a routine visit to the patient's own family physician (152 +/- 18/87 +/- 11). The coefficient of correlation between the systolic/diastolic ABP and the automated office BP (r = 0.62/0.72) was higher (P < 0.001) than with the family physician's manual BP (r = 0.32/0.48). The prevalence of white coat hypertension in untreated patients (n = 146) was significantly (P < 0.001) lower with automated office BP (16%) compared with the routine family physician BP (55%). CONCLUSION: The white coat response associated with office BP measurements can be virtually eliminated by recording BP with the automated BpTRU device with patients resting alone in a quiet examining room.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".