The association between vascular complications during pregnancy in women with Type 1 diabetes and congenital malformations
Bibliographic record
Abstract
AIMS: To assess the association between vascular complications of diabetes and the risk of congenital malformations in pregnant women with Type 1 diabetes. METHODS: We conducted an observational retrospective cohort study in women with Type 1 diabetes who received care consecutively from three tertiary care diabetes-in-pregnancy clinics in Calgary, Alberta, Canada. Multivariable logistic regression was used to assess the association between vascular complications (retinopathy, nephropathy and pre-existing hypertension) and congenital malformations in offspring of women with Type 1 diabetes. RESULTS: Of 232 women with Type 1 diabetes, 49 (21%) had at least one vascular complication and there were 52 babies with congenital malformations. Maternal age (31.8 ± 5.0 vs. 29.4 ± 4.7 years, P < 0.01), diabetes duration (20.9 ± 6.7 vs. 11.2 ± 7.4 years, P < 0.01) and pre-eclampsia rate (12.5% vs. 1.3%, P < 0.01) were higher in mothers with vascular complications than in those without. Multivariable analyses showed that vascular complications were not associated with an increased risk of congenital malformations (odds ratio 1.16, 95% confidence interval 0.46 to 2.88). CONCLUSIONS: Vascular complications are common, occurring in one-fifth of pregnant women with Type 1 diabetes, and in this study do not appear to be associated with an increased risk of congenital malformations in children.
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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 | 0.001 | 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".