Wave-intensity analysis: a new approach to coronary hemodynamics
Bibliographic record
Abstract
In 10 anesthetized dogs, we measured high-fidelity left circumflex coronary (P(LCx)), aortic (P(Ao)), and left ventricular (P(LV)) pressures and left circumflex velocity (U(LCx); Doppler) and used wave-intensity analysis (WIA) to identify the determinants of P(LCx) and U(LCx). Dogs were paced from the right atrium (control 1) or right ventricle by use of single (control 2) and then paired pacing to evaluate the effects of left ventricular contraction on P(LCx) and U(LCx). During left ventricular isovolumic contraction, P(LCx) exceeded P(Ao), paired pacing increasing the difference. Paired pacing increased DeltaP(X) (the P(LCx)-P(Ao) difference at the P(Ao)-P(LV) crossover) and average dP(LCx)/dt (P < 0.0001 for both). During this time, WIA identified a backward-going compression wave (BCW) that increased P(LCx) and decreased U(LCx); the BCW increased during paired pacing (P < 0.0001). After the aortic valve opened, the increase in P(Ao) caused a forward-going compression wave that, when it exceeded the BCW, caused U(LCx) to increase, despite P(LV) and (presumably) elastance continuing to increase. Thus WIA identifies the contributions of upstream (aortic) and downstream (microcirculatory) effects on P(LCx) and U(LCx).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".