Correlation between pre‐operative metabolic syndrome and persistent blood glucose elevation during cardiac surgery in non‐diabetic patients
Bibliographic record
Abstract
BACKGROUND: Cardiopulmonary-bypass (CPB) induces hyperglycemia. There is growing evidence that perioperative maintenance of blood glucose within the physiological range improves patients' outcome. Nevertheless, perioperative normoglycemia is often difficult to achieve during surgery with CPB and the response to insulin infusion is characterized by a considerable variability. The aim of this study was to determine to what extent the presence of pre-operative metabolic syndrome (MS) influences the blood glucose and insulin response during cardiac surgery. METHODS: Forty-five patients scheduled for elective cardiac surgery were screened for the presence of MS according to the International Diabetes Federation definition. Patients were then assigned to two groups: those with metabolic syndrome (MSP) and those without (control). During surgery, blood glucose levels were measured in all patients and hyperglycemia was treated with a standard protocol of continuous insulin infusion. RESULTS: The mean blood glucose levels during CPB increased only in the MSP group (P<0.001). Mean blood glucose in control patients did not increase during CPB (P=0.4). Patients with MS received 13.3+/-8.4 IU of insulin during CPB, while the control group did not require insulin treatment (P<0.001). Forty percent of patients in the control group and 100% of those in the MSP group developed post-operative insulin resistance. C-reactive protein was higher in the MSP group before, during and at 48 h after surgery. CONCLUSIONS: The mean blood glucose levels during CPB increased only in patients with MS, while they remained unchanged in patients in the control 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".