Method for evaluation of trustworthiness of oscillometric blood pressure measurements
Bibliographic record
Abstract
Simple, unobtrusive, and reliable estimation of cardiovascular parameters is a challenge. We present a novel, simple, and noninvasive method called Ratio2 that provides expected ranges for systolic and diastolic blood pressure values (SBP, DBP) estimated by any algorithm, and an evaluation of vessel compliance. Ratio2 was developed in the frame of the oscillometric blood pressure estimation and it exploits the equality between the arterial blood pressure and the cuff pressure at the mean arterial pressure (MAP). This method is based on the observation that the brachial arterial blood pressure pulses, with MAP used as baseline, are characterized by a peak to trough ratio close to 2. This ratio is employed to characterize expected ranges for estimates of systolic and diastolic blood pressure. Any SBP or DBP measurement which is not contained in these intervals is deemed untrustworthy, and it is marked as such. Ratio2 also provides parameters that are used in a mathematical model of arterial blood pressure (BP) to evaluate vessel stiffness. We tested the performance of the Ratio2 method on 150 oscillometric recordings and their corresponding Omron BP estimates obtained from 10 healthy subjects. Results are encouraging, whereby, (a) out-of-range values obtained with the maximum amplitude algorithm (MAA) and the maximum/minimum slope algorithm (MMSA) methods were successfully detected, and (b) linear correlation between age and vessel compliance is -85% (p<;0.005). Therefore, we conclude that the proposed work shows promise towards providing noninvasive BP monitors with an inbuilt mechanism for assessing the fidelity of their BP estimates along with an indicator of vessel compliance.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".