Pregnancy planning in women with pregestational diabetes
Bibliographic record
Abstract
OBJECTIVES: Women with pregestational diabetes are advised to plan their pregnancies to optimize glycemia and reduce fetal complications. We evaluated the adequacy of pregnancy planning effort and medical planning in pregnant women with type 1 and type 2 diabetes. METHODS: This retrospective cohort study surveyed pregnant women with pregestational diabetes mellitus between 2006 and 2008 in Ontario, Canada. We evaluated three measures of pregnancy planning: pregnancy planning effort, medical planning based on prepregnancy glycemic control, and folic acid use. We compared women with type 1 and type 2 diabetes and explored predictors of pregnancy planning. RESULTS: Of the 163 women studied (89 type 1, 74 type 2 diabetes), 47% reported high pregnancy planning effort, 58% reported attempts to optimize glycemic control, and 56% took folic acid before pregnancy. Of those who reported high pregnancy planning, 20% did not medically plan their pregnancies. Rates were similar between women with type 1 and type 2 diabetes. The most important predictor of pregnancy planning was having discussed plans with their physician. CONCLUSIONS: Our findings suggest that pregnancy planning is suboptimal in women with both type 1 and type 2 diabetes, highlighting a need to improve preconception counseling for all women with pregestational diabetes.
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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 | 0.001 | 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) | 0.002 | 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".