Benefits of Real-Time Continuous Glucose Monitoring in Pregnancy
Bibliographic record
Abstract
In recent years, continuous glucose monitoring (CGM) has become increasingly available with the introduction of devices that are specifically approved for use during pregnancy. Evidence in the form of randomized-controlled trials and cohort studies continues to build support for the use of CGM during pregnancy to improve measures of maternal glycemia as well as obstetric and neonatal outcomes. Based on data from the CGM in pregnant women with type 1 diabetes (CONCEPTT) trial alongside a Swedish cohort study of real-world outcomes of pregnant women with type 1 diabetes, the UK National Institute for Health and Clinical Excellence (NICE) guidelines now recommend that real-time CGM be offered to all pregnant women with type 1 diabetes. Based on these guidelines, all pregnant individuals in the United Kingdom with type 1 diabetes will receive government-funded real-time CGM for a 12-month duration. These guidelines are a game-changer and will continue to facilitate more widespread access to CGM use in the United Kingdom and beyond. This review describes the role of CGM in the management of diabetes in pregnancy, discusses contemporary maternal glucose levels and their relationship with outcomes in diabetes pregnancies, and examines the high-quality, randomized-controlled trial and the real-world clinical data evaluating the impact of CGM 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".