Manual and automated office measurements in relation to awake ambulatory blood pressure monitoring
Bibliographic record
Abstract
BACKGROUND: Automated blood pressure (BP) devices are commonly used in doctor's offices. How BP measured on these devices relates to ambulatory BP monitoring is not clear. OBJECTIVE: To assess how well office-based manual and automated BP predicts ambulatory BP. METHODS: Using data on 654 patients, we assessed how well sphygmomanometer measurements and measurements taken with an automated device (BpTRU) predicted results on ambulatory BP monitoring. We assess positive and negative predictive values and overall accuracy. We look at different cut-points for systolic (130, 135 and 140 mmHg) and diastolic (80, 85 and 90 mmHg) BP. RESULTS: A single automated office BP (AOBP) assessment provides superior predictive values and overall accuracy compared to three manual office BP assessments. For systolic BP, the predictive values are ≤69% for any of the cut-points while the positive predictive values for the single automated measurement is between 80.0% and 86.9% and the overall accuracy gets as high as 74% for the 130 mmHg cut-point. For diastolic BP, the automated readings are also more predictive but in this case, it is the negative predictive values that are better, as well as the overall accuracy. CONCLUSIONS: Based on the results, we suggest that 135/85 mmHg continue to be used as the cut-point defining high BP with the BpTRU device. However, future research might suggests that values in a grey zone between 130-139 mmHg systolic and 80-89 mmHg diastolic be confirmed using ambulatory BP monitoring. As well, three AOBP assessments might produce much greater accuracy than the single AOBP assessment used in the study.
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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 | 0.003 | 0.024 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".