Wave reflections in the pulmonary arteries analysed with the reservoir–wave model
Bibliographic record
Abstract
Key points In the pulmonary artery, we use the reservoir–wave model to separate the effects of a charging and discharging, elastic arterial reservoir from the effects of waves created by the contracting and relaxing heart. Wave intensity analysis quantifies the effects of waves that cause changes in pressure and flow and precisely identifies when waves created by the heart and reflections of these waves start and end. We show that negative wave reflections arise from the junction of lobar arteries stemming from the left and right pulmonary arteries. When blood volume is increased and pulmonary arteries become distended, the strength of negative wave reflections increases when 100% O2 is used for ventilation. Negative reflections suck blood downstream and, as they arrive when the heart is developing maximal pressure, negative reflections help to lower the back pressure the heart must pump against and, thus, they tend to increase the forward flow of blood. Abstract Conventional haemodynamic analysis of pressure and flow in the pulmonary circulation yields incident and reflected waves throughout the cardiac cycle, even during diastole. The reservoir–wave model provides an alternative haemodynamic analysis consistent with minimal wave activity during diastole. Pressure and flow in the main pulmonary artery were measured in anaesthetized dogs and the effects of hypoxia and nitric oxide, volume loading and positive end‐expiratory pressure were observed. The reservoir–wave model was used to determine the reservoir contribution to pressure and flow and once subtracted, resulted in ‘excess’ quantities, which were treated as wave‐related. Wave intensity analysis quantified the contributions of waves originating upstream (forward‐going waves) and downstream (backward‐going waves). In the pulmonary artery, negative reflections of incident waves created by the right ventricle were observed. Overall, the distance from the pulmonary artery valve to this reflection site was calculated to be 5.7 ± 0.2 cm. During 100% O2 ventilation, the strength of these reflections increased 10% with volume loading and decreased 4% with 10 cmH2O positive end‐expiratory pressure. In the pulmonary arterial circulation, negative reflections arise from the junction of lobar arteries from the left and right pulmonary arteries. This mechanism serves to reduce peak systolic pressure, while increasing blood flow.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".