Risk factors for postoperative hyperglycemia in neonates
Bibliographic record
Abstract
OBJECTIVE: Postoperative hyperglycemia has been shown to be associated with higher morbidity and mortality in pediatric patients. Data on risk factors for neonatal patients is limited. The objective of this study was to identify pre- and intraoperative risk factors associated with postoperative glucose in neonates. METHODS: We conducted a retrospective cohort study of neonates after surgical procedures between January and December 2016 in a quaternary neonatal intensive care unit. The primary outcome was hyperglycemia defined as serum glucose ≥8.3 mmol/L during the first 4 hours postoperatively. Secondary outcomes included death and length of stay. We assessed the association of risk factors with the postoperative glucose. RESULTS: In total, 206 surgical procedures (171 patients) were evaluated, among which 178 had serum glucose values during the first 4 hours postoperatively available. The incidence of hyperglycemia was 54% (n = 96). The median (IQR) glucose during the first 4 hours in NICU was 8.4 (6.52-10.65) mmol/L. Risk factors for postoperative hyperglycemia were intraoperative glucose infusion rate (GIR) and gestational age. There was a non-linear relationship between gestational age and postoperative hyperglycemia. Mortality occurred in 6 (7%) in the no-hyperglycemia group and 3 (3%) in the hyperglycemia group (p = 0.31). CONCLUSIONS: Among the risk factors, intraoperative GIR was identified as a modifiable factor that can reduce postoperative hyperglycemia. A non-linear relationship of gestational age with postoperative glucose provides new insights that may help advance our understanding of the complex mechanisms of glucose homeostasis in neonates.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".