Medication prescribing and pregnancy-related risk factors for women with type 2 diabetes of reproductive age within primary care: a cross-sectional investigation for the PREPARED study
Bibliographic record
Abstract
INTRODUCTION: Women with type 2 diabetes are at risk of commencing pregnancy while using medications that are either not recommended for pregnancy or with known teratogenicity, which may contribute to adverse pregnancy outcomes. In this study, we aimed to characterize pregnancy-related risk factors and medication exposures among women with type 2 diabetes. RESEARCH DESIGN AND METHODS: Individual health characteristics, sociodemographic information, and prescription data were extracted from the primary care records of women aged 18-45 years with type 2 diabetes in participating general practices in the UK. Prescribed medications were categorized according to suitability for pregnancy: recommended, not recommended, or not recommended but used if clinically indicated. Logistic regression was used to estimate associations between individual characteristics and medications not recommended for pregnancy. RESULTS: Data on 725 women were extracted. Prescribed medications suggested the presence of numerous comorbidities, with diabetes medications (65%, n=471) and statins (20%, n=145) most frequently prescribed. 37% (n=268) of women took ≥3 medications, and a third (n=269) took medications not recommended for pregnancy. Among those not prescribed contraception (89%, n=646), no one met all clinically recommended pre-pregnancy criteria. In multivariable logistic regression analysis, polypharmacy (OR 3.49 95% CI 2.88 to 4.30) and age (OR 1.04 95% CI 1.00 to 1.09) were associated with use of medications not recommended for pregnancy. CONCLUSIONS: Women with type 2 diabetes have suboptimal contraceptive provision despite multiple exposures to medications not recommended for pregnancy. Regular assessment of contraceptive use, reproductive intentions, and medication review is urgently needed in primary care settings to minimize pregnancy-related risks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".