Preoperative carbohydrate loading in patients undergoing coronary artery bypass or spinal surgery
Bibliographic record
Abstract
Surgery induces a state of insulin resistance (IR) which may be aggravated by current fasting practices. Oral preoperative carbohydrate (CHO) loading may reduce postoperative IR and subsequently complications. The current study was designed to determine whether CHO loading would blunt the development of postoperative IR, reduce preoperative discomfort and improve clinical outcomes in elective coronary artery bypass or spinal surgical patients. Thirty‐eight patients were randomized to receive a CHO supplement the evening before (100g CHO) and two hours prior to surgery (50g CHO) or fast for 12 hours preoperatively. Insulin sensitivity was measured using the short insulin tolerance test and homeostasis model assessment (HOMA). Patient discomfort was measured immediately before surgery using visual analog scales. Insulin sensitivity was not significantly different between groups. However, the treated group experienced a significantly smaller rise in glucose levels following surgery (p=0.03) and had higher postoperative HOMA‐β scores (p=0.02). Supplemented patients were also significantly less thirsty (p=0.01), hungry (p=0.04) and anxious (p=0.01) before surgery and experienced a significantly shorter hospital stay (p=0.008). CHO loading improved outcomes, warranting re‐evaluation of fasting practices in this population. This research was supported by a CIHR student fellowship.
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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 | 0.000 |
| 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".