Metabolic acidosis during continuous glucagon therapy for neonatal hypoglycemia
Bibliographic record
Abstract
Objectives: Refractory neonatal hypoglycemia may be treated with glucagon infusions, which have been associated with thrombocytopenia and hyponatremia. After anecdotally noting metabolic acidosis during glucagon therapy in our hospital, an outcome not previously reported in the literature, we aimed to quantify occurrence of metabolic acidosis (base excess >-6) as well as thrombocytopenia and hyponatremia during treatment with glucagon. Methods: We performed a single-centre retrospective case series. Descriptive statistics were used and subgroups compared with Chi-Square, Fisher's Exact Test, and Mann-Whitney U testing. Results: Sixty-two infants (mean birth gestational age 37.2 weeks, 64.5% male) were treated with continuous glucagon infusions for median 10 days during the study period. 41.2% were preterm, 21.0% were small for gestational age, and 30.6% were infants of diabetic mothers. Metabolic acidosis was seen in 59.6% and was more common in infants who were not born to diabetic mothers (75% versus 24% in infants of diabetic mothers, P<0.001). Infants with versus without metabolic acidosis had lower birth weights (median 2,743 g versus 3,854 g, P<0.01) and were treated with higher doses of glucagon (0.02 versus 0.01 mg/kg/h, P<0.01) for a longer duration (12.4 versus 5.9 days, P<0.01). Thrombocytopenia was diagnosed in 51.9% of patients. Conclusions: In addition to thrombocytopenia, metabolic acidosis of unclear etiology appears to be very common with glucagon infusions for neonatal hypoglycemia, especially in lower birth weight infants or those born to mothers without diabetes. Further research is needed to elucidate causation and potential mechanisms.
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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 | 0.001 | 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".