Estimation of pulse transit time using two diametric blood pressure waveform measurements
Bibliographic record
Abstract
This paper presents a novel method to estimate the aortic-to-peripheral pulse transit time (PTT) using the blood pressure waveforms measured at two diametric peripheral locations, for instance, one on an upper extremity and the other on a lower extremity. The method is based on a computational relationship between the two peripheral blood pressures, which is derived by first relating each peripheral blood pressure to a common central aortic blood pressure. The parameters of the computational relationship for an individual subject can be identified by fitting them to two peripheral blood pressure waveform measurements, thereby characterizing the cardiovascular dynamics, from which absolute changes in the PTT between the central aorta and each peripheral measurement site can be determined. The strength of the method is that it does not require any a priori knowledge of the peripheral measurement locations nor of the physiologic condition of the cardiovascular system. Experimental results are provided from five healthy swine subjects whose actual PTT experimentally varied from 44.9 ms to 163.0 ms (76.7 ms mean+/-26.4 ms SD). Compared to the reference PTT measurement, the novel method proposed in this paper estimated PTT within 3.7 ms mean+/-4.2 ms SD after initial 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".