Use of an automated blood pressurerecording device, the BpTRU, to reduce the“white coat effect” in routine practice
Bibliographic record
Abstract
BACKGROUND: Patients often exhibit higher blood pressure (BP) readings in the doctor's office, a phenomenon known as the white coat effect. This study examines the presence of a physician in the examining room as a possible factor in provoking a white coat effect. METHODS: Blood pressure measurements taken by an automated BP recording device, the BpTRU (VSM MedTech Ltd., Vancouver, BC, Canada) with the patient alone in the examining room, were compared with the following: (1) BP taken by the patient's family physician; (2) BP taken on the first visit to a hypertension specialist; (3) BP measured by a trained research technician and (4) the mean awake ambulatory BP (ABP). The BpTRU and trained research technician readings were taken outside of the office (treatment) setting in an ABP research unit. RESULTS: Blood pressure readings (mm Hg, mean +/- SEM) taken by the BpTRU (155 +/- 5/88 +/- 2) tended to be lower than for the family physician (166 +/- 4/89 +/- 3) and the hypertension specialist (174 +/- 5/92 +/- 2; P <.001). However, BP taken by the trained research technician (158 +/- 4/90 +/- 2) was similar to the value obtained by the BpTRU. The mean awake ABP was lower (P < 0.01) than the other four BP values. CONCLUSIONS: Use of an automated BP recording device outside of the office (treatment) setting can partly eliminate the white coat effect. A similar finding was observed with readings taken by a trained research technician under similar conditions. Referral of patients to nonoffice settings for automated BP recordings may provide a more accurate estimate of a patient's BP status, with partial elimination of the white coat effect associated with readings taken by a physician.
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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 | 0.002 | 0.016 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".