Recent advances in the antepartum management of diabetes
Bibliographic record
Abstract
Gestational and pre-gestational diabetes are frequent problems encountered in obstetrical practice and their complications may influence both the mother (such as hypertension, pre-eclampsia, increased caesarean rates) and the foetus (such as macrosomia, shoulder dystocia, respiratory distress, hypoglycaemia, or childhood obesity and diabetes). Given the important implications for mothers and their offspring, screening and appropriate management of diabetes during pregnancy are essential. This is a review of articles published between 2015 and 2018 on Medline via Ovid that focus on advances in the management of diabetes in pregnancy. Recent data have concentrated predominantly on optimising glycaemic control, which is key for minimising the burden of maternal and foetal complications. Lifestyle changes, notably physical exercise and diet adjustments, appear to have beneficial effects. However, data are inconclusive with respect to which diet and form of exercise provide optimal benefits. Oral glycaemic agents-in particular, metformin-are gaining acceptance as more data indicating their long-term safety for the foetus and newborn emerge. Recent reviews present inconclusive data on the efficacy and safety of insulin analogues. New technologies such as continuous insulin pumps for type 1 diabetes and telemedicine-guided management of diabetes are significantly appreciated by patients and represent promising clinical tools. There are few new data addressing the areas of antenatal foetal surveillance, the timing and need for induction of delivery, and the indications for planned caesarean section birth.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".