Abstract P221: Physicians are More Prone to Causing White Coat Hypertension than Nurses or Cardiovascular Technicians: An Observational Study
Bibliographic record
Abstract
Introduction: Accurate assessments of blood pressure (BP) are critical for the effective diagnosis and treatment of hypertension. A substantial portion of patients labelled hypertensive have been shown to instead have White Coat Hypertension (WCH), where their BP is elevated exclusively when assessed in a clinic. In this study, we assessed whether the type of healthcare provider measuring individuals’ blood pressure impacted the incidence of WCH. Methodology: Following collection of baseline demographics, 106 participants had their BP measured by a physician, a nurse and a cardiovascular technician. The order of measurements was randomized. All healthcare providers used the same BP cuff for measurements and were instructed to measure BP following a standardized method. Following BP readings taken by healthcare providers, a 24-hour Ambulatory Blood Pressure Monitor (ABPM) was applied to all participants. The average of the daytime readings of the ABPM served as the control for this study. Results: Patients whose BP were greater than 140/90 mm Hg when measured by a healthcare provider, but whose control readings by ABPM were less than 135/85 mm Hg were classified as having WCH. Physicians caused 33% of participants (35 of 106) to have WCH. Nurses caused 23.5% of participants (25 of 106) to have WCH. Cardiovascular technicians caused 5.6% of participants (6 of 106) to have WCH. (p<0.0001). Similar trends were observed based on analysis examining the percentage of accurate readings and the average of readings compared to the control ABPM, with technicians having the most accurate readings, nurses having moderately accurate readings, and physicians having the least accurate readings. Conclusions: The results of this study suggest that the incidence of WCH and BP measurement inaccuracy occur more frequently when BP is assessed by certain types of healthcare providers, potentially because patients may feel more anxious or stressed around these individuals. It may therefore be advisable for BP to be assessed by cardiovascular technicians, instead of nurses or physicians, to reduce the risk of White Coat Hypertension and inaccurate BP readings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".