The effects of changes in loading conditions and modulation of inotropic state on the myocardial performance index: comparison with conductance catheter measurements
Bibliographic record
Abstract
AIM: The myocardial performance index (MPI), or Tei index, has been shown to be useful in the assessment of global myocardial performance. There are few invasive data however, which examine its load dependence or sensitivity to acute changes in contractile function. The purpose of this study was therefore to study formally the effect of clinically relevant changes in these parameters in an animal model. METHODS AND RESULTS: We examined 10 Yorkshire pigs using echocardiographic assessment and simultaneous measurements of intraventricular pressure and volume by conductance catheterization. With dobutamine infusion, there was no significant change in the MPI (0.26+/-0.13-0.22+/-0.11; p=0.42), but dP/dtmax increased significantly (1001+/-240-1569+/-532 mm Hg/s, p<0.001). Afterload increase (40% change in ventricular pressure) and preload reduction (20% change in ventricular volume) were associated with significant increases in the MPI (0.26+/-0.13-0.49+/-0.20; p<0.005 and 0.26+/-0.13-0.51+/-0.20; p<0.001, respectively) without any significant change in maximal elastance (Ees). CONCLUSIONS: The MPI, or Tei index, is significantly affected by acute changes in loading conditions but is unable to consistently detect acute changes in contractile function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".