High glucose variability is associated with poor neurodevelopmental outcomes in neonatal hypoxic ischemic encephalopathy
Bibliographic record
Abstract
BACKGROUND: In neonatal hypoxic ischemic encephalopathy (HIE), hypo- and hyperglycemia have been associated with poor outcomes. However, glucose variability has not been reported in this population. OBJECTIVE: To examine the association between serum glucose variability within the first 24 hours and two-year neurodevelopmental outcomes in neonates cooled for HIE. STUDY DESIGN: In this retrospective cohort study, glucose, clinical and demographic data were documented from 23 term newborns treated with whole body therapeutic hypothermia. Severe neurodevelopmental outcomes from planned two-year assessments were defined as the presence of any one of the following: Gross Motor Function Classification System levels 3 to 5, Bayley III Motor Standard Score <70, Bayley III Language Score <70 and Bayley III Cognitive Standard Score <70. RESULTS: The neurodevelopmental outcomes from 8 of 23 patients were considered severe, and this group demonstrated a significant increase of mean absolute glucose (MAG) change (-0.28 to -0.03, 95% CI, p = 0.032). There were no significant differences between outcome groups with regards to number of patients with hyperglycemic means, one or multiple hypo- or hyperglycemic measurement(s). There were also no differences between both groups with mean glucose, although mean glucose standard deviation was approaching significance. CONCLUSIONS: Poor neurodevelopmental outcomes in whole body cooled HIE neonates are significantly associated with MAG changes. This information may be relevant for prognostication and potential management strategies.
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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 | 0.004 |
| 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 | 0.000 |
| 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".