Continuous Glucose Monitoring Versus Self-Monitoring of Blood Glucose to Assess Glycemia in Gestational Diabetes
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM) management using self-monitoring blood glucose (SMBG) does not normalize pregnancy outcomes. Objective: We aimed to conduct an observational study to explore if continuous glucose monitoring (CGM) could identify elevated glucose levels not apparent in women with GDM managed using SMBG. Study Design: A 7-day masked-CGM (iPro; Medtronic) was performed within 2 weeks of GDM diagnosis, immediately post-GDM education, but before insulin commencement as determined by SMBG. CGM data regarding hyperglycemia (sensor glucose >126 mg/dL [06:00–00:00 h] and >99 mg/dL [00:00–06:00 h] for >10% of time), time with health care professionals, treatment, and pregnancy outcome were collected. Comparisons (Mann–Whitney test) were performed between subjects subsequently commenced on insulin versus those continued with diet and lifestyle measures alone. Results: Ninety women of mean (standard deviation) gestational age weeks 27(1) were studied. Those prescribed insulin ( n = 34) compared with those managed with diet and lifestyle alone ( n = 56) had a greater time in hyperglycemia ( P = 0.0001). Of those not prescribed insulin, 35/56 (61%) breached CGM cutoffs between 00:00 and 06:00 h; 11/56 (20%) breached 6.00–00.00 h CGM cutoffs for >10% of the time; and 21/45 (47%) with optimal CGM glucose levels during the daytime spent >10% time in hyperglycemia between 00.00 and 06:00 h. In contrast, SMBG measurements exceeded the clinical targets of <120 mg/dL postdinner in 5.4% and <100 mg/dL fasting in 0% of the subjects. Conclusions: CGM provides a more comprehensive assessment of nocturnal hyperglycemia than SMBG and could improve targeting of interventions in GDM. Larger studies to better define CGM targets are required, which once established will inform studies aimed at targeting nocturnal glucose levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".