Anesthetic-induced Improvement of the Inflammatory Response to One-lung Ventilation
Bibliographic record
Abstract
BACKGROUND: Although one-lung ventilation (OLV) has become an established procedure during thoracic surgery, sparse data exist about inflammatory alterations in the deflated, reventilated lung. The aim of this study was to prospectively investigate the effect of OLV on the pulmonary inflammatory response and to assess possible immunomodulatory effects of the anesthetics propofol and sevoflurane. METHODS: Fifty-four adults undergoing thoracic surgery with OLV were randomly assigned to receive either anesthesia with intravenously applied propofol or the volatile anesthetic sevoflurane. A bronchoalveolar lavage was performed before and after OLV on the lung side undergoing surgery. Inflammatory mediators (tumor necrosis factor alpha, interleukin 1beta, interleukin 6, interleukin 8, monocyte chemoattractant protein 1) and cells were analyzed in lavage fluid as the primary endpoint. The clinical outcome determined by postoperative adverse events was assessed as the secondary endpoint. RESULTS: The increase of inflammatory mediators on OLV was significantly less pronounced in the sevoflurane group. No difference in neutrophil recruitment was found between the groups. A positive correlation between neutrophils and mediators was demonstrated in the propofol group, whereas this correlation was missing in the sevoflurane group. The number of composite adverse events was significantly lower in the sevoflurane group. CONCLUSIONS: This prospective, randomized clinical study suggests an immunomodulatory role for the volatile anesthetic sevoflurane in patients undergoing OLV for thoracic surgery with significant reduction of inflammatory mediators and a significantly better clinical outcome (defined by postoperative adverse events) during sevoflurane anesthesia.
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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) | 0.000 | 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".