Optimum waveform envelopes and amplitude ratios in oscillometric blood pressure estimation
Bibliographic record
Abstract
OBJECTIVE: To determine if, when using the oscillometric method, there is a specific range of amplitude ratios in the fixed-ratio algorithm that will result in blood pressure estimates that consistently fall within a mean error ≤5 mmHg and a SD of the error <8 mmHg. Additionally, to apply different representations of the oscillometric waveform envelope to verify if this will affect the accuracy of the results. METHODS: SBP and DBP were obtained using the fixed-ratios method applied to a dataset of 219 oscillometric measurements obtained from 73 healthy volunteers and compared to their corresponding auscultation values. Ratio and envelope analysis were done on Matlab (The MathWorks, Inc., Natick, Massachusetts, USA). RESULTS: Depending on the envelope representation, ratios between 0.44-0.74 for systolic pressure and 0.51-0.85 for diastolic pressure yield results within the limits mentioned above. When a set of optimum envelope representations and ratios are selected based on population mean, the highest percentage of subjects presenting blood pressure estimates within the limits were 72.6% for systolic and 69.9% for diastolic. CONCLUSION: The range of ratios presenting optimum results appears to be independent of the degree of arterial stiffness given the wide range of ages of the subjects in the study. Different representations of the oscillometric waveform envelope may improve the accuracy of the method. However, there remains a considerable percentage of the population with unreliable results. It is therefore important to only use devices that have been properly validated according to standard protocol.
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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 | 0.007 | 0.052 |
| 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 | 0.002 | 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".