Subject-Specific Estimation of Central Aortic Blood Pressure Using an Individualized Transfer Function: A Preliminary Feasibility Study
Bibliographic record
Abstract
This paper presents a new approach to the estimation of unknown central aortic blood pressure waveform from a directly measured peripheral blood pressure waveform, in which a physics-based model is employed to solve for a subject- and state-specific individualized transfer function (ITF). The ITF provides the means to estimate the unknown central aortic blood pressure from the peripheral blood pressure. Initial proof-of-principle for the ITF is demonstrated experimentally through an in vivo protocol. In swine subjects taken through wide range of physiologic conditions, the ITF was on average able to provide central aortic blood pressure waveforms more accurately than a nonindividualized transfer function. Its usefulness was most evident when the subject's pulse transit time deviated from normative values. In these circumstances, the ITF yielded statistically significant reductions over a nonindividualized transfer function in the following three parameters: 1) 30% reduction in the root-mean-squared error between estimated versus actual central aortic blood pressure waveform (p < 10 (-4)), 2) >50% reduction in the error between estimated versus actual systolic and pulse pressures ( p < 10 (-4)), and 3) a reduction in the overall breakdown rate (i.e., the frequency of estimation errors >3 mmHg, p < 10 (-4)). In conclusion, the ITF may offer an attractive alternative to existing methods that estimates the central aortic blood pressure waveform, and may be particularly useful in nonnormative physiologic conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".