Risk factors for ventilator-induced-lung injury develop three to five times faster after a single episode of lung injury
Bibliographic record
Abstract
Introduction: Mechanical ventilator breaths provided to deeply sedated patients have an abnormal volume distribution, encouraging alveolar collapse in dependent regions and promoting alveolar overdistention in non-dependent regions. Collapse and overdistention both start with the first breath and worsen over time, driving ventilator-induced lung injury (VILI). This is exacerbated when the lung is already injured or has increased heterogeneity. Our study investigated the impact of a single episode of lung injury on lung mechanics and the risk factors for ventilator-induced injury, compared with non-injured lungs. Methods: O, with respiratory rate and FiO2 set to maintain normal blood gas values. Animals in one group were ventilated for 50 h (50-Hour MV group, n=10). Animals in the second group had lung injury induced using oleic acid and were ventilated for 12 h post-injury (LI MV group, n=6). Both groups were compared with a never-ventilated control group (NV, n=6). Lung mechanics and injury were measured using electrical impedance tomography, esophageal pressure monitoring and tissue histology. Results: End-expiratory lung-volume loss was greater in the 50-Hour MV group (P<0.05). Plateau pressure, driving pressure and lung injury score were higher in the LI MV group, (P<0.05). Conclusion: Risk factors for VILI developed three- to five-times faster in the group with injured lungs, demonstrating that a single lung-injury episode substantially increased the risk of VILI, compared with normal lungs, despite using a lung-protective mechanical ventilation protocol.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".