Early blood glucose screening in asymptomatic high-risk neonates
Bibliographic record
Abstract
OBJECTIVES: Detecting and treating severe hypoglycemia promptly after birth is crucial due to its association with adverse long-term neurodevelopmental outcomes. However, limited data are available on the optimal timing of glucose screening in asymptomatic high-risk neonates prone to hypoglycemia. Risk factors associated with asymptomatic high-risk neonates include late prematurity ≥35 and <37 weeks gestation (LPT), small-for-gestational-age (SGA), large-for-gestational-age (LGA), and infant-of-a-diabetic mother (IDM). This study aims to determine the incidence and the impact of individual risk factors on early hypoglycemia (defined as blood glucose ≤25 mg/dL in the initial hour after birth) in asymptomatic high-risk neonates. METHODS: All asymptomatic high-risk neonates ≥35 weeks gestation underwent early blood glucose screening within the first hour after birth (n=1,690). A 2-year retrospective analysis was conducted to assess the incidence of early neonatal hypoglycemia in this cohort and its association with hypoglycemia risk factors. RESULTS: Out of the 9,919 births, 1,690 neonates (17 %) had risk factors for neonatal hypoglycemia, prompting screening within the first hour after birth. Incidence rates for blood glucose ≤25 mg/dL and ≤15 mg/dL were 3.1 and 0.89 %, respectively. Of concern, approximately 0.5 % of all asymptomatic at-risk neonates had a blood glucose value of ≤10 mg/dL. LPT and LGA were the risk factors significantly associated with early neonatal hypoglycemia. CONCLUSIONS: Asymptomatic high-risk neonates, particularly LPT and LGA neonates, may develop early severe neonatal hypoglycemia identified by blood glucose screening in the first hour of life. Additional investigation is necessary to establish protocols for screening and managing asymptomatic high-risk 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".