Accuracy of cuff blood pressure and systolic blood pressure amplification
Bibliographic record
Abstract
Abstract Automated cuff measured blood pressure (BP) is the global standard used for diagnosing hypertension, but there are concerns regarding the accuracy of the method. Individual variability in systolic BP (SBP) amplification from central (aorta) to peripheral (brachial) arteries could be related to the accuracy of cuff BP, but this has never been determined and was the aim of this study. Automated cuff BP and invasive brachial BP were recorded in 795 participants (74% male, aged 64 ± 11 years) receiving coronary angiography at five independent research sites (using seven different automated cuff BP devices). SBP amplification was recorded invasively by catheter and defined as brachial SBP minus aortic SBP. Compared with invasive brachial SBP, cuff SBP was significantly underestimated (130 ± 18 mmHg vs. 138 ± 22 mmHg, p < 0.001). The level of SBP amplification varied significantly among individuals (mean ± SD, 7.3 ± 9.1 mmHg) and was similar to level of difference between cuff and invasive brachial SBP (mean difference –7.6 ± 11.9 mmHg). SBP amplification explained most of the variance in accuracy of cuff SBP (R2 = 19%). The accuracy of cuff SBP was greatest among participants with the lowest SBP amplification (ptrend < 0.001). After cuff BP values were corrected for SBP amplification, there was a significant improvement in the mean difference from the intra-arterial standard (p < 0.0001) and in the accuracy of hypertension classification according to 2017 ACC/AHA guideline thresholds (p = 0.005). The level of SBP amplification is a critical factor associated with the accuracy of conventional automated cuff measured BP.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".