Raised IL-2 and TNF-α concentrations are associated with postoperative delirium in patients undergoing coronary-artery bypass graft surgery
Bibliographic record
Abstract
BACKGROUND: The knowledge base regarding the pathogenesis of postoperative delirium is limited. The primary aim of this study is to investigate whether increased levels of IL-2 and TNF-α are associated with delirium in patients who underwent coronary-artery bypass graft (CABG) surgery with cardiopulmonary bypass (CPB). The secondary aim is to establish whether any association between raised cytokine levels and delirium is related to surgical and anesthetic procedures or mediated by pre-existing conditions associated with raised cytokine levels, such as major depressive disorder (MDD), cognitive impairment, or aging. METHODS: Patients were examined and screened for MDD and cognitive impairment one day preoperatively, using the Mini International Neuropsychiatric Interview and The Montreal Cognitive Assessment and Trail Making Test Part B. Blood samples were collected postoperatively for cytokine levels. RESULTS: Postoperative delirium screening was found positive in 36% (41 of 113) of patients. A multivariate logistic regression revealed that an increased concentration of pro-inflammatory cytokines is associated with delirium, and related to advancing age, preoperative cognitive decline of participants, and duration of CPB. According to receiver operating characteristic analysis, the most optimal cut-off for IL-2 and TNF-α concentrations in predicting the development of delirium were 907.5 U/ml and 10.95 pg/ml, respectively. CONCLUSIONS: The present study suggests that raised postoperative cytokine concentrations are associated with delirium after CABG surgery. Postoperative monitoring of pro-inflammatory markers combined with regular surveillance may be helpful in the early detection of postoperative delirium in this patient group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".