EP08.40: Determining the hospital costs of macrosomia after diabetes in pregnancy
Bibliographic record
Abstract
To determine the influence of macrosomia on neonatal and maternal hospital resource utilisation following diabetes in pregnancy. This was a retrospective cohort of singleton pregnancies complicated by pre-existing diabetes at a regional maternity hospital (2011 to 2020). Primary exposure was macrosomia (birthweight above the 90 percentile for gestational age). A validated electronic hospital database was used to collate patient demographics, pregnancy complications, birth events, and neonatal outcomes. The main outcome was peripartum hospital resource utilisation for neonates and mothers as measured by length of hospital stay, intensive care unit admission, and resource intensity weight (RIW) - a proxy of healthcare costs. Descriptive and inferential statistics were used to evaluate the relationship between macrosomia and hospital resource utilisation. 1241 pregnancies were included (87.5% with type 2 diabetes). Incidence of macrosomia was 48.5%, including one-third with birthweights above the 97 percentile. Younger maternal age, multiparity, and cigarette smoking were more common in the macrosomia group. Significantly more newborns with macrosomia required NICU admission than compared to appropriately grown controls (42% vs 29%, p < 0.0001), despite no difference in gestational age at birth, Caesarean section, or other neonatal complications. Hospital stays were longer for neonates with macrosomia (10.7 days (SD 20.7) vs 8.4 days (SD 16.8), p = 0.036) and their mothers (4.4 days (SD 3.6) vs 3.8 days (SD 3.0), p = 0.0003), but there was no difference in resource intensity weights for those with macrosomia compared to diabetes alone (p = 0.726). Macrosomia confers an additional burden on hospital resources beyond diabetes in pregnancy alone. Better understanding of the hospital care required after macrosomia will improve allocation of limited healthcare resources and counselling of families experiencing this common pregnancy complication.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".