A New Approach to Improve the Hemodynamic Assessment of Cardiac Function Independent of Respiratory Influence
Bibliographic record
Abstract
Abstract Cardiovascular and respiratory systems are anatomically and functionally linked; inspiration produces negative intrathoracic pressures that act on the heart and alter cardiac function. Inspiratory pressures increase with heart failure and can exceed the magnitude of ventricular pressure during diastole. Accordingly, respiratory pressures may be a confounding factor to assessing cardiac function. While the interaction between respiration and the heart is well characterized, the extent to which systolic and diastolic indices are affected by inspiration is unknown. Our objective was to understand how inspiratory pressure affects the hemodynamic assessment of cardiac function. To do this, we developed custom software to assess and separate indices of systolic and diastolic function into inspiratory, early expiratory, and late expiratory phases of respiration. We then compared cardiac parameters during normal breathing and with various respiratory loads. Variations in inspiratory pressure had a small impact on systolic pressure and function. Conversely, diastolic pressure strongly correlated with negative inspiratory pressure. Cardiac pressures were less affected by respiration during expiration; late expiration was the most stable respiratory phase. In conclusion, inspiration is a large confounding influence on diastolic pressure, but minimally affects systolic pressure. Performing cardiac hemodynamic analysis by accounting for respiratory phase yields more accuracy and analytic confidence to the assessment of diastolic 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".