Safety of Insulin Glargine Use in Pregnancy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes in women of childbearing age is increasing. As such, the number of pregnancies complicated by diabetes will inevitably increase. New insulin analogues such as the long-acting analogue insulin glargine may represent beneficial treatment options in pregnancy by ensuring that patients achieve excellent glycemic control without risk of maternal hypoglycemia. OBJECTIVE: To determine the fetal safety of insulin glargine use in the treatment of diabetes in pregnancy compared with NPH insulin therapy. METHODS: A systematic review and meta-analysis was performed of all original human studies that reported neonatal outcomes among women with pregestational or gestational diabetes who were managed with either insulin glargine or NPH insulin during pregnancy. A systematic literature search was conducted using MEDLINE, EMBASE, CINAHL, the Cochrane Central Register for Controlled Trials database, and Web of Science from 1980 to June 1, 2010. Outcomes included large size for gestational age, macrosomia, neonatal hypoglycemia, neonatal intensive care unit admissions, birth trauma, congenital anomalies, preterm delivery, perinatal mortality, respiratory distress, and hyperbilirubinemia. Relative risk ratios and weighted mean differences were computed with 95% confidence intervals. RESULTS: Eight studies reporting on a total of 702 women with pregestational or gestational diabetes in pregnancy treated with either insulin glargine (n = 331) or NPH insulin (n = 371) met the inclusion criteria. There were no statistically significant differences in the occurrence of fetal outcomes studied with the use of insulin glargine compared to NPH insulin. CONCLUSIONS: No evidence has been documented for increased adverse fetal outcomes with the use of insulin glargine in pregnancy compared to the use of NPH insulin. These results increase the choices for women requiring basal insulin therapy in pregnancy.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".