A new statistical phase offset technique for the calculation of in vivo pulse wave velocity
Bibliographic record
Abstract
Pulmonary blood pressure measurements were collected from 5 clinically healthy horses.Pulse wave velocity (PWV) values were calculated using five techniques, four existing (minimum foot-to-foot, F2F; maximum 1st derivative, M1D; maximum 2nd derivative, M2D; and cross correlation, CC) and the new statistical phase offset technique (SPO).The SPO technique was also applied to systolic (SPO-S), diastolic (SPO-D) and full wave (SPO-FW) data.The reliability of each analysis technique was determined using the consistency of calculated PWV values.Using the original data sets, of variable length (2 n 5) due to the effects of respiration, the SPO technique gave the most consistent results (SPO-D, 2.31 Æ 0.31 m/s; SPO-S, 2.18 Æ 0.30 m/s; and SPO-FW, 2.45 Æ 0.35 m/s).The CC technique, was complex to implement but also gave considerable consistency (CC, 2.64 Æ 0.36 m/s).The family of techniques utilizing only a single point of comparison all provided less consistent results (M1D, 2.82 Æ 0.56 m/s; M2D, 2.90 Æ 1.09 m/s; and F2F, 3.42 Æ 1.67 m/s).Consistent length data sets were then created (n Z 5) and analyzed.Results were: SPO-S, 2.74 Æ 0.34 m/s; SPO-D, 2.67 Æ 0.40 m/s; SPO-FW, 2.78 Æ 0.36 m/s; F2F, 2.53 Æ 0.52 m/s; M1D, 3.39 Æ 1.28 m/s; M2D, 3.20 Æ 1.90 m/s; and CC, 3.23 Æ 0.40 m/s.Comparison of the results indicate that of the techniques included in this study, the new SPO technique provided the greatest reliability for determining PWV values.It was also intuitive to implement.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".