Veno-venous ECMO as a platform to evaluate lung lavage and surfactant replacement therapy in an animal model of severe ARDS
Bibliographic record
Abstract
BACKGROUND: There are limited therapeutic options directed at the underlying pathological processes in acute respiratory distress syndrome (ARDS). Experimental therapeutic strategies have targeted the protective systems that become deranged in ARDS such as surfactant. Although results of surfactant replacement therapy (SRT) in ARDS have been mixed, questions remain incompletely answered regarding timing and dosing strategies of surfactant. Furthermore, there are only few truly clinically relevant ARDS models in the literature. The primary aim of our study was to create a clinically relevant, reproducible model of severe ARDS requiring extracorporeal membrane oxygenation (ECMO). Secondly, we sought to use this model as a platform to evaluate a bronchoscopic intervention that involved saline lavage and SRT. METHODS: Yorkshire pigs were tracheostomized and cannulated for veno-venous ECMO support, then subsequently given lung injury using gastric juice via bronchoscopy. Animals were randomized post-injury to either receive bronchoscopic saline lavage combined with SRT and recruitment maneuvers (treatment, n = 5) or recruitment maneuvers alone (control, n = 5) during ECMO. RESULTS: after aspiration injury was 62.6 ± 8 mmHg and 60.9 ± 9.6 mmHg in the control and treatment group, respectively (p = 0.95) satisfying criteria for severe ARDS. ECMO reversed the severe hypoxemia. After treatment with saline lavage and SRT during ECMO, lung physiologic and hemodynamic parameters were not significantly different between treatment and controls. CONCLUSIONS: A clinically relevant severe ARDS pig model requiring ECMO was established. Bronchoscopic saline lavage and SRT during ECMO did not provide a significant physiologic benefit compared to controls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".