A new paradigm for lung-conservative total liquid ventilation
Bibliographic record
Abstract
BACKGROUND: Total liquid ventilation (TLV) of the lungs could provide radically new benefits in critically ill patients requiring lung lavage or ultra-fast cooling after cardiac arrest. It consists in an initial filling of the lungs with perfluorocarbons and subsequent tidal ventilation using a dedicated liquid ventilator. Here, we propose a new paradigm for a lung-conservative TLV using pulmonary volumes of perfluorocarbons below functional residual capacity (FRC). METHODS AND FINDINGS: Using a dedicated technology, we showed that perfluorocarbon end-expiratory volumes could be maintained below expected FRC and lead to better respiratory recovery, preserved lung structure and accelerated evaporation of liquid residues as compared to complete lung filling in piglets. Such TLV below FRC prevented volutrauma through preservation of alveolar recruitment reserve. When used with temperature-controlled perfluorocarbons, this lung-conservative approach provided neuroprotective ultra-fast cooling in a model of hypoxic-ischemic encephalopathy. The scale-up and automating of the technology confirmed that incomplete initial lung filling during TLV was beneficial in human adult-sized pigs, despite larger size and maturity of the lungs. Our results were confirmed in aged non-human primates, confirming the safety of this lung-conservative approach. INTERPRETATION: This study demonstrated that TLV with an accurate control of perfluorocarbon volume below FRC could provide the full potential of TLV in an innovative and safe manner. This constitutes a new paradigm through the tidal liquid ventilation of incompletely filled lungs, which strongly differs from the previously known TLV approach, opening promising perspectives for a safer clinical translation. FUND: ANR (COOLIVENT), FRM (DBS20140930781), SATT IdfInnov (project 273).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".