Non-invasive determination of instantaneous brachial blood flow using the oscillometric method
Bibliographic record
Abstract
The oscillometric method has been widely used to measure arterial systolic and diastolic blood pressures, but its potential for arterial blood flow measurements still remains to be explored. The aim of this study was to non-invasively determine arterial blood flow using an oscillometric blood flow measurement system. The system consists of a pneumatic elastic cuff, an air-pumping motor, a releaser valve, a pressure transducer, and an airflow meter. To build a non-linear cuff model, we measured airflow pumped into the pneumatic cuff and cuff pressure using an airflow meter and pressure transducer during the inflation period, respectively. During the deflation period, only the pressure transducer was used to record cuff pressure. Based on the cuff model, the oscillometric blood flow waveform was obtained by integrating the oscillometric pressure waveform. We compared arterial blood flow derived from the maximum amplitude of the oscillometric blood flow waveform with Doppler-measured blood flow calculated with the diameters and blood velocities of the brachial arteries in 32 subjects who underwent diagnostic evaluations for peripheral arterial embolism. A linear correlation coefficient of r = 0.716 was found between the oscillometry- and Doppler-based blood flow measurements in the 32 subjects. These results suggest that blood flow passing through the brachial artery can be quantified non-invasively using the oscillometric approach after appropriate calibration.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".