Inflammatory Mediators in Pericardial Fluid in Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
The pericardial space provides a homeostatic environment that facilitates optimal cardiac function. The pericardial space contains pericardial fluid (PCF) and other tissue sources, including pericardial adipose tissue and the great vessels. Given its proximity to the heart, PCF has emerged as a potential diagnostic, prognostic, and therapeutic vehicle. As such, the biochemical and humoral characteristics of PCF have recently been the focus of several studies. Evidence shows that the PCF is a rich reservoir for various hormones, cytokines, adhesion molecules, and multiple other substances. This review aims to better understand the pericardial microenvironment, focusing on the kinetic and dynamic changes that govern different inflammatory molecules in the PCF in patients undergoing cardiac surgery. Our electronic search yielded 7 studies that reported the changes in PCF levels of interleukin (IL)-1, IL-6, IL-8, IL-10, tumour necrosis factor (TNF)α, interferon (IFN)γ, and vascular endothelial growth factor (VEGF) during or in the immediate postoperative period after cardiac surgery. Although it was not possible to make direct comparisons of inflammatory marker levels across studies because of inconsistencies in their reporting, we aimed to identify dynamic changes in pericardial levels of these inflammatory mediators, with a focus on their potential role in the development of postoperative inflammatory response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".