Continuous Glucose Monitoring Time-in-Range and HbA <sub>1c</sub> Targets in Pregnant Women with Type 1 Diabetes
Bibliographic record
Abstract
The CONCEPTT trial compared real-time Continuous Glucose Monitoring (RT-CGM) to capillary glucose monitoring in pregnant women with type 1 diabetes. We analyzed CGM and glycated hemoglobin (HbA 1c ) measures in first ( n = 221), second ( n = 197), and third ( n = 172) trimesters, aiming to examine target glucose attainment and associations with pregnancy outcomes. CGM targets were Time-in-range (TIR) > 70%, Time-above-range (TAR) <25%, and Time-below-range (TBR) < 4%, and HbA 1c targets < 6.5% (National Institute for Health and Care Excellence [NICE]) and HbA 1c < 6.0% in second and third trimesters (American Diabetes Association [ADA]). TIR/TAR/TBR targets were achieved by 7.7/14.5/30.3% participants in first, 10.2/14.2/52.8% in second, and 35.5/37.2/52.9% in third trimesters. CGM target attainment was low but increased during pregnancy and with RT-CGM use. In the adjusted analyses, achieving TBR target was associated with a higher risk of pre-eclampsia and neonatal hypoglycemia. ADA HbA 1c target attainment was low and unchanged during pregnancy (23.5/27.9/23.8%) but increased with RT-CGM use. In the adjusted analyses, HbA 1c target attainment was associated with a lower risk of preterm birth, large-for-gestational age and neonatal hypoglycemia. We conclude that CONCEPTT trial participants had a low rate of CGM and of HbA 1c target attainment. Attainment of CGM and NICE HbA 1c targets increased throughout gestation and all targets (both NICE/ADA HbA 1c and CGM) were more likely to be achieved by RT-CGM users, at 34 weeks' gestation. ADA HbA 1c target achievement was independently associated with better perinatal outcomes, while the independent association of TBR target achievement with increased risk warrants further study. ClinicalTrials.gov Registration Identifier NCT01788527.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".