Routine use of Corticosteroids to Prevent Inflammation Response in Cardiac Surgery
Bibliographic record
Abstract
Cardiac surgery, especially if it involves cardiopulmonary bypass, is associated with a severe systemic inflammatory response. It is characterized by complement activation and initiation of coagulation, fibrinolysis and kallikrein cascades. Consecutive activation of immunoregulatory cells results in an extensive release of pro- and anti-inflammatory cytokines. This inflammatory storm is related to organ dysfunction or failure and correlates with postoperative morbidity. In order to attenuate this deleterious inflammatory response in the perioperative period alternative surgical techniques, novel extracorporeal circulation devices and immunomodulatory pharmacological strategies are in focus of contemporary research. Since decades corticosteroids have been used and studied in patients undergoing cardiac surgery. Although it could be shown that glucocorticoids seem to change the pro-inflammatory cytokine profile in a favourable manner, it still remains controversial if this effect translates into a better clinical outcome. Several clinical trials have proclaimed an association between this inflammatory response and the incidence of major complications i.e, myocardial infarction and pulmonary complications, but until now they have failed to show conclusive results. This article describes the different types and recommended dose schemes of corticosteroids in the perioperative period of cardiac surgery along with the discussion of few patents. It will comment on potential side effects and review the effect on the postoperative outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".