Should pregnant women with diabetes be counseled differently if nephropathy was detected? a population database study
Bibliographic record
Abstract
INTRODUCTION: The prevalence of diabetes mellitus has increased tremendously in the last two decades among women of reproductive age and this is mainly due to the pandemic of obesity. Diabetes mellitus is a well-known cause of maternal and neonatal complications in pregnancy. Diabetic nephropathy is a marker of severe diabetes and results in organ damage. However, only a small number of studies have evaluated the implications of diabetic nephropathy on pregnancy complications, with most having 50 to 100 nephropathy subjects. Our study aims to compare pregnant women with diabetes mellitus complicated by nephropathy or not and evaluate the relationship with obstetrical and perinatal morbidity and mortality, on a larger population. METHODS: This was a population-based study using data from the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) including women who delivered between 2004 and 2014. Multivariate logistic regression was used to control for confounding effects. RESULTS: Among 86,615 pregnancies that were complicated by diabetes mellitus, 1,241 (1.4%) had diabetic nephropathy. Diabetic nephropathy was strongly associated with preeclampsia (aOR 2.3, 95% CI 1.90-2.68), as well as chronic hypertension with superimposed preeclampsia or eclampsia (aOR 4.2, 95% CI 3.53-5.01), preterm birth (aOR 1.8, 95% CI 1.59-2.1), and blood transfusion (aOR 3.6 95% CI 2.82-4.46). Both groups were similar in age and income. CONCLUSION: Diabetic nephropathy is associated with increased obstetrical and perinatal morbidity compared to diabetes mellitus alone. These patients may benefit from a high dose of folic acid, more vigilant antenatal surveillance, delivery in a tertiary care center, and more rigorous screening and prevention methods for pregnancy-induced hypertension diseases at antenatal care visits.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".