Ventilation Induces Changes in Pulse Wave Transit Time in the Pulmonary Artery
Bibliographic record
Abstract
Pulse wave transit time (PWTT) shortens as pulmonary artery pressure (PAP) increases and was therefore suggested as a surrogate parameter for PAP. The aim of this analysis was to reveal patterns and potential mechanisms of ventilation-induced periodic changes in PWTT under resting conditions. To measure both PWTT and PAP in five healthy pigs, two pulmonary artery Mikro-Tip™ catheters were inserted into the pulmonary vasculature: one with the tip placed in the pulmonary artery trunk, and a second one placed in a distal segment of the pulmonary artery. Animals received pressure-controlled mechanical ventilation. Ventilation-dependent changes were seen in both variables, PWTT and mean PAP; however, changes in PWTT were not synchronous with changes in PAP. Thus, plotting the value of PWTT for each heartbeat over the respective PAP revealed a characteristic hysteresis. At the beginning of inspiration, PAP rose while PWTT remained constant. During further inspiration, PWTT started to decrease rapidly as mPAP was about to reach its plateau. The same time course was observed during expiration: while mPAP approached its minimum, PWTT increased rapidly. During apnea this hysteresis disappeared. Thus, non-synchronous ventilation-induced changes in PWTT and PAP were found with inspiration causing a significant shortening of PWTT. Therefore, it is suggested that the respiratory cycle should be considered when using PWTT as a surrogate for PAP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".