Hypoglycemia in unmonitored full-term newborns—a surveillance study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hypoglycemia monitoring is not recommended for most full-term newborns. We wished to determine the incidence, presentation and case characteristics of hypoglycemia in low-risk newborns. METHODS: With the assistance of the Canadian Paediatric Surveillance Program, we conducted a national study of severe hypoglycemia in apparently low-risk full-term newborns. Paediatricians who reported a case were sent a detailed questionnaire and the data were analyzed. RESULTS: All 93 confirmed cases were singletons, 56% were first-borns and 65% were male. An 8% rate of First Nations cases was twofold the population rate. Maternal hypertension rate was 23%, fourfold the general pregnancy rate. Maternal obesity was double the general pregnancy rate at 23%. Concerning signs or feeding issues were noted in 98% at the time of diagnosis. Median time to diagnosis was 4.1 hours. Mean blood glucose at intravenous (IV) start was 1.4 ± 0.5 hours (SD). Seventy-eight per cent had at least one of four potential stress indicators and were more likely to have early diagnosis (P=0.03). Major signs were present in 20%. Those cases presented later and had lower glucose levels (median=0.8 mmol/L versus 1.6 mmol/L, [P<0.001). Twenty-five per cent of cases had birth weight less than the 10th centile. Neurodevelopmental concern was reported in 20%. Of the 13 cases which had brain magnetic resonance imaging, 11 were abnormal. CONCLUSION: Hypoglycemia in unmonitored newborns is uncommon but is associated with significant morbidity. We provide a range of clues to help identify these newborns soon after birth. Widespread adoption of norm-based standards to identify small-for-gestational age infants is supported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".