Glycemic Stress Index: Does It Correlate with the Intensive Care Length of Stay?
Bibliographic record
Abstract
Postoperative hyperglycemia is an independent risk factor for postoperative complications. In adults, perioperative hyperglycemia is influenced by prolonged fasting, but data in children are lacking. The Glycemic Stress Index (GSI) has been shown to predict prolonged Pediatric Intensive Care Unit (PICU) stays in neurosurgical patients. This study aimed to confirm the correlation between GSI and duration of intubation, PICU stay, and postoperative complications in infants undergoing elective open heart surgery. The correlation between preoperative fasting and GSI was also investigated. METHODS: A retrospective chart review of 85 infants ≤ 6 months undergoing elective open heart surgery was performed. GSI values ≥ 3.9 and 4.5 were tested to determine whether they carried a higher incidence of postoperative complications (metabolic uncoupling, kidney injury, ECMO, and death). The correlation between GSI and the length of intubation, PICU stay, and duration of fasting were also investigated. Perioperative factors such as age, weight, blood gas analysis, use of inotropes, and risk adjustment for congenital heart surgery were also analyzed as possible predictors. RESULTS: GSI correlated with the duration of intubation and PICU stay. A GSI ≥ 4.5, but not 3.9, was associated with a higher incidence of metabolic uncoupling. GSI was not influenced by preoperative fasting. None of the preoperative patient factors analyzed was associated with prolonged intubation, PICU stay, or PICU complications. An abnormal creatinine before surgery increased the risk of developing acute kidney injury postoperatively. CONCLUSIONS: GSI may be valuable to predict prolonged intubation, PICU stay, and metabolic derangement in infants undergoing cardiac surgery. Fasting does not appear to affect GSI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".