Preoperative values of inflammatory markers predict clinical outcomes in patients after CABG, regardless of the use of cardiopulmonary bypass
Bibliographic record
Abstract
Objective The impact of systemic inflammation on clinical outcomes after CABG surgery is still controversial. In this study, we evaluated the impact of the markers of inflammation, endothelial damage and platelet activation on clinical outcomes after on- and off-pump CABG. Methods A group of 191 consecutive on- and off-pump CABG patients were prospectively studied. Blood samples were drawn before surgery, 18–36 h after the procedure and 5–7 days postoperatively and analyzed for 8-iso-prostaglandin F 2α (8-iso-PGF 2α ), asymmetric dimethylarginine (ADMA) and β-thromboglobulin (β-TG). White blood count and C-reactive protein were measured twice, first before and then during the first 18–36 h after CABG. The primary clinical end-points were: low cardiac output syndrome (LCOS), postoperative myocardial infarction (PMI) and in-hospital cardiovascular death. Results Elevation of 8-iso-PGF 2α , ADMA and β-TG before surgery was associated with an increased risk of morbidity and mortality after CABG. There were no differences in analyzed markers and clinical outcomes between the on- and off-pump groups. Even during the uncomplicated postoperative course the inflammatory response was enhanced and still remained higher than baseline 5–7 days after surgery. Conclusion Links between preoperative 8-iso-PGF 2α , ADMA and β-TG and unfavorable early post-CABG outcomes suggest that these markers could be useful in identifying patients with increased risk of LCOS, PMI and in-hospital cardiovascular death following elective CABG.
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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 | 0.001 |
| 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".