Pilot Assessment of Piezoelectric Transducers as a Cost-Effective Alternative to EndoPAT
Bibliographic record
Abstract
INTRODUCTION: Current microvascular assessments may not be practical or accessible requiring experienced personnel and/or ongoing equipment costs. Piezoelectric transducers can reliably obtain finger blood pressure waves, similar to peripheral arterial tonometry devices; thus, they could be used to estimate microvascular function. We aimed to validate piezoelectric transducers as an alternative measure of microvascular function compared to EndoPAT. METHODS: Twenty-five adults (aged 20-64 years) completed reactive hyperemia (5 min forearm circulatory occlusion and 3 min recovery) with piezoelectric transducers on the middle fingers and EndoPAT probes on the index fingers. Average area under the curve (AUC) of the pulse wave signal for the occluded and control arms was determined at baseline, every 30 s post-occlusion, and 10 s around the peak response. Microvascular function index (MFI) was calculated as the ratio of AUC post-occlusion to AUC baseline in the test arm, then normalized to the same ratio in the control arm. MFI at each time point was correlated with the reactive hyperemia index (RHI) from the EndoPAT. RESULTS: The greatest significance was found between RHI and MFI at 10 s around the peak response (Spearman's r = 0.67, p = 0.0002; Pearson's r = 0.76, p = 0.00001). CONCLUSION: MFI is a reusable and user-friendly microvascular function assessment that could provide better access to vascular health screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".