Accuracy of oscillometric blood pressure algorithms in healthy adults and in adults with cardiovascular risk factors
Bibliographic record
Abstract
BACKGROUND: Fixed-ratio and slope-based algorithms are used to derive oscillometric blood pressure (BP). However, a paucity of published data exists assessing the accuracy of these methods. Our objective was to determine the accuracy of fixed-ratio and slope-based algorithms in healthy adults and in adults with cardiovascular risk factors. PATIENTS AND METHODS: Overall, 85 healthy adults (age≥18 years) and 85 adults with cardiovascular risk factors were studied. Three oscillometric and four two-observer mercury-based auscultation measurements were performed in each, according to International Standards Organization 2013 methodology. Two fixed-ratio algorithms and one slope-based algorithm were applied to process oscillometric waveform envelopes and derive oscillometric BP. Paired and unpaired t-tests were used to compare mean oscillometric BP within and between each group, respectively. RESULTS: For healthy adults, mean age was 50.3±17.8 years, mean arm circumference was 30.4±3.8 cm, and 62% were female. In the cardiovascular risk group, mean age was 63.8±12.4 years, mean arm circumference was 31.9±4.2 cm, and 62% were female. For systolic BP, the fixed-ratio algorithms produced the lowest mean error and narrowest SD. For diastolic BP, mean errors were similar for all three algorithms, but the fixed-ratio algorithms had higher precision. The comparison of healthy adults and those with cardiovascular risk factor showed high variability for systolic and diastolic BP (SD: 8.113.9 mmHg). CONCLUSION: In both healthy adults and in those with cardiovascular risk factors, the fixed-ratio technique performed better than the slope-based algorithm. High between-group variability indicates that subject-specific algorithms may be needed.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".