I14 Monitoring for hypoglycaemic newborns – should we expand our risk categories?
Bibliographic record
Abstract
Background and objectives Universal hypoglycaemia monitoring of newborns is not recommended. We wished to determine the incidence, presentation and case characteristics of hypoglycaemic newborns that were not being monitored. Methods Through the Canadian Paediatric Surveillance Program we conducted a national study of severe hypoglycaemia in apparently low-risk full-term newborns. Inclusion criteria were: an otherwise healthy infant less than 96 hours old; gestational age 37–42 weeks; birth weight 2500–3999 grams; whole blood or serum glucose less than 2.0 mmol/L and IV dextrose used to treat the hypoglycaemia. Data were managed and analysed using IBM SPSS Statistics for Windows, Version 24.0 (Armonk, NY: IBM Corp.). Results From April 2014 to March 2016, 177 cases were reported. There were 5 duplicates, 33 cases did not meet inclusion criteria and 46 questionnaires were not returned, leaving 93 confirmed cases. The estimated incidence was 1 in 8378 births. All cases were singletons, 56% were first-borns and 65% were male. An 8% rate of First Nations cases was 3-fold the population rate. Maternal hypertension was present in 23%, 4-fold the overall pregnancy rate. Maternal obesity was double the overall pregnancy rate at 23%. Concerning signs or feeding issues were present at diagnosis in 98%. Median time to diagnosis was 4.1 hours. Mean blood glucose was 1.4±0.5 hours (SD). Seventy eight percent had at least one of 4 potential perinatal stress indicators (emergency Caesarean Section, meconium at delivery, requiring resuscitation or cord artery pH <7.10). Those cases were more likely to be diagnosed before 6 hours (p=0.03). Twenty five percent (23 cases) were small for gestational age (SGA) with birth weight <10 th centile, of which 5 had seizures and 5 had hyperinsulinism. Presentation with major clinical signs (seizure, apnoea or cyanosis) occurred in 20%. Neurodevelopmental concern was present in 20% of all cases. Amongst 13 cases which had brain MRI, 11 were abnormal. Conclusion While acknowledging the study’s limitations, the impact of First Nations origin, maternal obesity, maternal hypertension and perinatal stress indicators warrant further study and possible incorporation into glucose monitoring guidelines. The data further supports adoption of norm-based standards to identify and monitor all SGA infants. Funding Supported by grants from Queen’s University Faculty of Health Sciences and the Public Health Agency of Canada.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".