P3.02 DESCRIBING WAVES IN THE PULMONARY VEINS: APPLICATION OF A RESERVOIR-WAVE MODEL
Bibliographic record
Abstract
Abstract Background The pulmonary venous pressure waveform is typically described by the downstream events in the left atrium and ventricle. These downstream events create waves that contribute to the overall waveform. Methods In anesthetised open-chest dogs, measurements of pressure and flow were made in the pulmonary artery and vein. Experiments involved increases to blood volume and the application of 10 cm H2O positive end-expiratory pressure (PEEP). The reservoir-wave model describes the reservoir pressure, which is subtracted from measured pressure, to result in the excess pressure (Pexcess). Excess velocity (Uexcess) is similarly formulated. Pexcess and Uexcess are used in wave intensity analysis to calculate wave speed and separate the contributions of waves originating upstream (forward waves) and downstream (backward waves). Results Separated waves are shown in the bottom panel of Figure 1. The effect of PEEP resulted in larger decreases to Pbackward (p < 0.001) after the mitral valve opened. As a result, y was lower than x by ~2.0 mmHg. With PEEP, the delay between arterial and venous forward waves increased from 155 ± 4 ms to 183 ± 4 ms (mean ± SE, p < 0.001). Conclusion The majority of pulmonary venous pressure landmarks can be attributed to the actions of the left atrium and ventricle but the v wave has substantial contributions from waves originating in the pulmonary artery. Diastolic suction has a larger effect with PEEP, presumably from some external constraint applied to the heart and consequently lowered end-systolic left ventricular volume. Figure 1 Common venous markers related to measured pressures (top panel) and the separation of Pexcess into forward and backward components (bottom panel) at control conditions.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".