Mechanical Ventilation with Lower Tidal Volumes and Positive End-expiratory Pressure Prevents Pulmonary Inflammation in Patients without Preexisting Lung Injury
Bibliographic record
Abstract
BACKGROUND: Mechanical ventilation with high tidal volumes aggravates lung injury in patients with acute lung injury or acute respiratory distress syndrome. The authors sought to determine the effects of short-term mechanical ventilation on local inflammatory responses in patients without preexisting lung injury. METHODS: Patients scheduled to undergo an elective surgical procedure (lasting > or = 5 h) were randomly assigned to mechanical ventilation with either higher tidal volumes of 12 ml/kg ideal body weight and no positive end-expiratory pressure (PEEP) or lower tidal volumes of 6 ml/kg and 10 cm H2O PEEP. After induction of anesthesia and 5 h thereafter, bronchoalveolar lavage fluid and/or blood was investigated for polymorphonuclear cell influx, changes in levels of inflammatory markers, and nucleosomes. RESULTS: Mechanical ventilation with lower tidal volumes and PEEP (n = 21) attenuated the increase of pulmonary levels of interleukin (IL)-8, myeloperoxidase, and elastase as seen with higher tidal volumes and no PEEP (n = 19). Only for myeloperoxidase, a difference was found between the two ventilation strategies after 5 h of mechanical ventilation (P < 0.01). Levels of tumor necrosis factor alpha, IL-1alpha, IL-1beta, IL-6, macrophage inflammatory protein 1alpha, and macrophage inflammatory protein 1beta in the bronchoalveolar lavage fluid were not affected by mechanical ventilation. Plasma levels of IL-6 and IL-8 increased with mechanical ventilation, but there were no differences between the two ventilation groups. CONCLUSION: The use of lower tidal volumes and PEEP may limit pulmonary inflammation in mechanically ventilated patients without preexisting lung injury. The specific contribution of both lower tidal volumes and PEEP on the protective effects of the lung should be further investigated.
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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) | 0.000 | 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) | 0.001 | 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".