A Randomized Trial of Carbohydrate Preloading in Patients with Type 2 Diabetes Undergoing General Anesthesia
Bibliographic record
Abstract
Objective: The objective of the study was to determine the efficacy and safety of preoperative carbohydrate (CHO) loading among patients with type 2 diabetes (T2D) undergoing low to intermediate risk surgery. Materials and methods: A randomized controlled trial was conducted among 50 T2D patients on oral hypoglycemic drugs selected based on the American Society of Anesthesiologists (ASA) grade 2, posted for low to intermediate risk surgeries. Twenty-five participants were randomly allocated to group A (carbohydrate group) and group B (placebo group). Patient well-being in terms of visual analog scale (VAS) scores for hunger, thirst, and postoperative vomiting was assessed. Mean plasma glucose was the primary outcome, gastric volume and pH and VAS scores were secondary outcomes. Results: Clinical variables such as age, gender, body mass index (BMI), fasting plasma glucose (FPG), random plasma glucose (RPG), glycated hemoglobin (HbA1c), surgical duration, fluids, and opioids administered were comparable between both groups (p > 0.05).The mean plasma glucose levels in the postoperative period at 0 hour in group A and group B was 19.32 mg/dL and 30.13 mg/dL respectively and the difference was statistically significant (p = 0.008). At 10 hours post-surgery, the mean plasma glucose of group A (20.04 mg/dL) was significantly lower than group B (28.5 mg/dL) (p = 0.035). Secondary outcomes in both groups did not show any significant difference (p > 0.05). Conclusions: The improved glycemic control and insulin resistance was observed in the carbohydrate loading group, with no adverse effects, resulting in improved outcomes among patients with T2D undergoing surgery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".