PS-BPC10-3: DETERMINATION OF AORTIC STIFFNESS USING TWO ESTABLISHED ALGORITHMS
Bibliographic record
Abstract
Objectives: Arterial stiffness is a risk factor for cardiovascular disease. Aortic stiffness is assessed by determination of pulse wave velocity using pulse transit time and the distance between carotid and femoral arteries. Transit time is obtained by using the foot-to-foot method to define the transit time through intersecting tangents algorithm or the point of maximal upstroke during systole (2nd derivative). Millasseau et al have proposed a formula for converting transit time between methods using SphygmoCor (intersecting tangents) and the Complior Analyse (2nd derivative). Based on a mathematical modeling of the proposed formula, there is discrepancy between values of pulse wave velocities, especially in subjects with higher aortic stiffness. The objective is to directly compare the two methods using the same pressure waveforms obtained by the newer generation of Complior Analyse and using Millasseau formula. Design and Methods: In a cross-sectional study of heterogeneous subjects, aortic stiffness was assessed by the Complior device which uses 2nd derivative. The pulse waveforms were extracted and used for the analysis by custom MATLAB algorithm for intersecting tangents, and the results were compared to the formula proposed by Millasseau. Results: The preliminary results of the first 24 patients (men: 71%; mean age: 61 ± 18 years) show that Millasseau formula underestimates the transit times values by about 19% in comparison with the transit times obtained by the intersecting tangents method using MATLAB software (50 ± 19 ms vs 62 ± 19 ms; P < 0.001). This results in an overestimation of the pulse wave velocities values by about 30% (13.8 ± 3.8 m/s vs 10.6 ± 2.7 m/s; P < 0.001). Conclusions: Our preliminary results allow us to conclude that the values of pulse wave velocities obtained with Millasseau formula overestimate values as compared to the values obtained by intersecting tangents method. Increasing the number of subjects will allow us to examine the possibility of a more reliable formula for converting transit times between methods.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".