Maternal BMI and HDL as predictors of pregnancy outcome in women with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: Diabetes in pregnancy is associated with increased risks of maternal as well as foetal complications. METHODS: Retrospective data on 96 women and their 96 newborns were anonymously statistically analysed to assess pregnancies of type 1 diabetes (T1D) women managed in our hospital in past nine years. The outcomes of the neonates were divided into three categories according to the clinical status, presence of congenital abnormalities and infant's treatment. RESULTS: We found out that the outcome of newborn infants associated with maternal HbA1c before gestation as well as during the whole course of pregnancy (p < 0.02 for all). Surprisingly, neonatal outcome was strongly associated with the maternal BMI (p < 0.05). In our model, a lowering of BMI by one grade led to an 18% increase in the chance that the newborn will have no health problems. We did not observe an important worsening of chronic diabetic complications in mothers; however, regarding maternal clinical status, we found that preeclampsia occurrence was strongly and independently connected to HDL level (p < 0.01). CONCLUSION: Our data demonstrate that lower pregestational BMI could substantially improve T1D mothers' pregnancy outcome. Lower HDL levels in T1D mothers during pregnancy correlate with higher risk of preeclampsia development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".