The necessity of the patient sitting alone in silence for the measurement of blood pressure using a BpTRU device
Bibliographic record
Abstract
Context: While previous studies have demonstrated an increase in blood pressure when measured while talking, the effect of conversation on the measurement of blood pressure using a BpTRU device is unknown. Objective: In adults who have their blood pressure measured using a BpTRU device in the primary care setting, does the presence of a healthcare professional engaging the patient in conversation during the measurement period affect the recorded blood pressure compared to the patient being alone in a private room and silent during the measurement period? Design: An assessment of two approaches of measuring blood pressure using a BpTRU device in which participants serve as their own control and the order of testing is randomized. Participants: Adults (n=272) aged ≥ 19 years recruited from an academic family practice unit in St. John’s NL. Intervention: Participant’s blood pressure was measured twice using a BpTRU device, once while alone in a quiet room maintaining silence and once while engaging in a health-related conversation. Whether blood pressure was measured first during conversation or during silence was randomized. Outcome Measures: Systolic and diastolic blood pressure, and the mean difference in systolic and diastolic blood pressures between talking and silent measurement conditions. Results: Mean systolic blood pressure was 9 mmHg higher (95% CI 8.2 – 10.5) and mean diastolic blood pressure was 8 mmHg higher (95% CI 7.6 – 8.9) when measured during conversation compared to when measured in silence. Systolic and diastolic blood pressure measured during conversation remained significantly higher after controlling for all other variables in multiple linear regression analysis. Conclusion: To avoid inaccurate measurement of blood pressure which could result in the overdiagnosis and overtreatment of hypertension, blood pressure measurement with the BpTRU device should be conducted with patients alone and in silence.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".