Volume loading reduces pulmonary vascular resistance in ventilated animals with acute lung injury: evaluation of RV afterload
Bibliographic record
Abstract
During mechanical ventilation, increased pulmonary vascular resistance (PVR) may decrease right ventricular (RV) performance. We hypothesized that volume loading, by reducing PVR, and, therefore, RV afterload, can limit this effect. Deep anesthesia was induced in 16 mongrel dogs (8 oleic acid-induced acute lung injury and 8 controls). We measured ventricular pressures, dimensions, and stroke volumes during positive end-expiratory pressures of 0, 6, 12, and 18 cmH(2)O at three left ventricular (LV) end-diastolic pressures (5, 12, and 18 mmHg). Oleic acid infusion (0.07 ml/kg) increased PVR and reduced respiratory system compliance (P < 0.05). With positive end-expiratory pressure, PVR was greater at a lower LV end-diastolic pressure. Increased PVR was associated with a decreased transseptal pressure gradient, suggesting that leftward septal shift contributed to decreased LV preload, in addition to that caused by external constraint. Volume loading reduced PVR; this was associated with improved RV output and an increased transseptal pressure gradient, which suggests that rightward septal shift contributed to the increased LV preload. If PVR is used to reflect RV afterload, volume loading appeared to reduce PVR, thereby improving RV and LV performance. The improvement in cardiac output was also associated with reduced external constraint to LV filling; since calculated PVR is inversely related to cardiac output, increased LV output would reduce PVR. In conclusion, our results, which suggest that PVR is an independent determinant of cardiac performance, but is also dependent on cardiac output, improve our understanding of the hemodynamic effects of volume loading in acute lung injury.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".