Postoperative insulin secretion is decreased in patients with preoperative insulin resistance
Bibliographic record
Abstract
BACKGROUND: Postoperative hyperglycemia is associated with increased rate of surgical site infection, renal failure, and cardiovascular events. The study of insulin sensitivity state before surgery could help in treating postoperative hyperglycemia and preventing iatrogenic hypoglycemia. We studied the postoperative insulin secretion in patients who have a low insulin sensitivity (IR) before surgery compared to patients with normal preoperative insulin sensitivity (IS). MATERIALS AND METHODS: Forty-two consecutive patients, undergoing abdominal surgery, underwent preoperative sequential hyperglycemic-euglycemic clamp (SHEC) in order to measure insulin secretion and to screen patients with low insulin sensitivity (IR) or with normal insulin sensitivity (IS). Patients had been randomized to receive either general anesthesia with epidural or PCA. RESULTS: Postoperative insulin secretion in IR patients is decreased compared to IS (P = 0.059) and to IR before surgery regardless to the type of analgesia (P < 0.001). In the IS group, postoperative insulin secretion depends on type of analgesia. It is increased when using PCA and decreased when using epidural (P < 0.05). Blood glucose increased after surgery in both IS an IR (P < 0.001). Patients with preoperative insulin resistance had a higher glycemia before and after surgery (P < 0.001). Blood glucose levels were comparable between PCA and epidural patients (P = 0.450). CONCLUSION: Insulin secretion is reduced in IR regardless the type of anesthesia. PCA increases insulin secretion, whereas epidural decreases it in patients with normal insulin sensitivity. These findings implicate that after surgery insulin administration is advisable in patients with preoperative insulin resistance while it should be given cautiously in those with normal preoperative insulin sensitivity.
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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 | 0.000 | 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 | 0.000 |
| 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".