A HEAD-TO-HEAD COMPARISON OF PULSE TRANSIT TIME USING SECOND-DERIVATIVE AND INTERSECTING TANGENTS METHODS
Bibliographic record
Abstract
Objective: Determination of aortic stiffness as measured by carotid-femoral pulse wave velocity (CFPWV) is based on pulse transit time (PTT). PTT is determined by identification of the foot of the pressure wave using either the peak of the second-derivative or the intersecting tangents algorithm. The aim of this study was to examine if there are any differences when these algorithms are performed on the same arterial pressure waveforms. Design and method: Carotid and femoral arterial pressure waveforms were simultaneously recorded using the piezoelectric sensors of the Complior Analyse system. Waveforms were extracted and imported into In-house software (MATLAB; MathWorks, Natick, Massachusetts, USA) to calculate the PTT only on valid pressure waveforms without artifacts that are due to movements. Results: In 105 human subjects (59 ± 18 years; 64% men) including a mix of 59% hypertensive, 34% with chronic kidney disease, 16% diabetic and 13% with cardiovascular disease, the average brachial blood pressure was 125 ± 18/73 ± 10 mmHg. A total of 6 203 pairs of simultaneous carotid and femoral arterial pressure waveforms were analysed. The PTT using the second-derivative algorithm was 68.4 ± 18.6 ms (CFPWV: 9.48 ± 3.18 m/s), similar to PTT using the intersecting tangents method that was 68.6 ± 18.8 (CFPWV: 9.45 ± 3.12). This difference while statistically significant given the high number of observation points (P < 0.001), is not considered to be clinically significant. Conclusions: Based on our findings, both algorithms perform similarly and can be used interchangeably when high quality acquisition are used. However, device-based algorithms may perform differently given variabilities in quality assessment criteria.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".