Preconception care and the risk of congenital anomalies in the offspring of women with diabetes mellitus: a meta-analysis
Bibliographic record
Abstract
Offspring of women with pregestational diabetes mellitus are at increased risk for congenital malformations, largely attributable to poor periconceptional glycaemic control. We assessed the effect of preconception care in reducing congenital malformations, in a meta-analysis of published studies of preconception care in women with diabetes mellitus. Articles were retrieved from Medline (1970 to June 2000) and Embase (1980 to June 2000), and data abstracted by two independent reviewers. The rates and relative risks (RR) for major and minor congenital malformations were pooled from all eligible studies using a random effects model, as were early first-trimester glycosylated haemoglobin values. In 14 cohort studies, major congenital malformations were assessed among 1192 offspring of mothers who had received preconception care, and 1459 offspring of women who had not. The pooled rate of major anomalies was lower among preconception care recipients (2.1%) than non-recipients (6.5%) (RR 0.36, 95%CI 0.22-0.59). In nine studies, the risk for major and minor anomalies was also lower among women who received preconception care (RR 0.32, 95%CI 0.17-0.59), as were the early first-trimester mean glycosylated haemoglobin values (pooled mean difference: 2.3%, 95%CI 2.1-2.4). Women who received preconception care were, on average, 1.8 years older than non-recipients, and fewer smoked (19.6% vs. 30.2%). Only one study described the routine use of periconception folic acid. Out-patient preconception care probably reduces the risk of major congenital anomalies among the offspring of women with pregestational diabetes mellitus. Because many women with diabetes neither plan their pregnancy nor achieve adequate glycaemic control before conception, strategies are needed to improve access to these programs, and to maximize those interventions associated with improved pregnancy outcome, such as smoking cessation and folic acid use.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".