Effects of liver ischemia-reperfusion injury on respiratory mechanics and driving pressure during orthotopic liver transplantation
Bibliographic record
Abstract
BACKGROUND: During orthotopic liver transplantation (OLT), liver graft ischemia-reperfusion injury (IRI) triggers a cytokine-mediated systemic inflammatory response, which impairs graft function and disrupts distal organ homeostasis. The objective of this prospective, observational trial was to assess the effects of IRI on lung and chest wall mechanics in the intraoperative period of patients undergoing OLT. METHODS: In 26 patients undergoing OLT, we measured elastance of the respiratory system (ERS), partitioned into lung (EL) and chest wall (ECW), hemodynamics, and fluid and blood product intake before laparotomy (T1), after portal/caval surgical clamp (T2), and immediately (T3) and, at 90 and 180 minutes post-reperfusion (T4 and T5, respectively). Interleukin-6 (IL-6), monocyte chemotactic protein-1 (MCP-1), IL-1β and tumor necrosis factor-α plasma concentrations were assessed at T1, T4 and T5. RESULTS: EL significantly decreased from T1 to T2 (13.5±4.4 vs 9.7±4.8 cmH2O/L, P<0.05), remained stable at T3, while at T4 (12.3±4.4 cmH2O/L, P<0.05) was well above levels recorded at T2, reaching its highest value at T5 (15±3.9 cmH2O/L, P<0.05). Variations in ERS, EL, driving pressure (∆P) and trans-pulmonary pressure (∆PL) significantly correlated with changes in IL-6 and MCP-1 plasma concentrations, but not with changes in wedge pressure, fluid amounts, and red blood cells and platelets administered. No correlation was found between changes in cytokine concentrations and ECW. CONCLUSIONS: We found that EL, ECW, ∆P and ∆PL underwent significant variations during the OLT procedure. Further, we documented a significant association between the respiratory mechanics changes and the inflammatory response following liver graft reperfusion.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".