Trends and associated maternal characteristics of antidiabetic medication use among pregnant women in South Korea
Bibliographic record
Abstract
Abstract The prevalence of diabetes during pregnancy and the need for the treatment are increasing. We aimed to investigate antidiabetic medications (ADM) use among pregnant women and their characteristics. Using Korea’s nationwide healthcare database, we included women aged 15–49 years with births during 2004–2013. The prevalence and secular trend of ADM use were assessed in 3 periods: pre-conception period, first trimester, and second/third trimesters. To compare maternal characteristics between pregnancies with and without ADM prescription, we used the χ2 or Fisher’s exact test and Cochran-Armitage trend test. The prescription patterns analyzed by calendar year, age, insurance type, income, area, and medical institution. Of 81,559 pregnancies, 222 (0.27%) and 305 (0.37%) were exposed ADM during pre-conception and pregnancy periods, respectively. ADM prescriptions increased significantly by an 11.3-fold in second/third trimesters, while a 2.9-fold in first trimester. ADM use is more prevalent in women aged older and living in urban areas. Metformin was most used in the pre-conception period, while insulins were most during pregnancy. About 0.4% of women received ADM during pregnancy; a rate was lower than that in western countries. Non-recommended medications were more common in first trimester, which warrants pregnancy screening for women taking ADM.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".