Circadian Variation in the Response to the Glucose Challenge Test in Pregnancy
Bibliographic record
Abstract
OBJECTIVE: A common approach to screening for gestational diabetes mellitus (GDM) is the universal testing of all pregnant women with a 1-h, 50-g glucose challenge test (GCT), followed by a diagnostic oral glucose tolerance test (OGTT) in those in whom the GCT is positive (≥7.8 mmol/L). More important, the GCT is performed at any time of day, but there has been limited study of the effect of time of day on test performance. Thus, using their subsequent OGTT (performed in the morning), we sought to characterize the metabolic function of women with positive GCTs in relation to the timing of their test. RESEARCH DESIGN AND METHODS: A total of 927 women with positive GCTs underwent a 3-h 100-g OGTT. They were stratified into four groups by time of day (hours) of their GCT: <0900 (n = 171), 0900-1059 (n = 288), 1100-1259 (n = 189), and ≥1300 (n = 279). RESULTS: On the OGTT, the prevalence of GDM progressively decreased across the GCT groups from <0900 h (26.9%) to 0900-1059 h (25.0%) to 1100-1259 h (21.7%) to ≥1300 h (21.5%; P = 0.0022). After adjustment for GDM risk factors, mean adjusted glucose area under the curve (AUC(gluc)) similarly decreased across the groups, while insulin sensitivity (Matsuda index) and β-cell function (Insulin Secretion-Sensitivity Index-2) progressively increased (all P < 0.0001). In particular, compared with the <0900- and 0900-1059-h groups, women whose positive GCT occurred after 1300 h had superior metabolic function, as evidenced by lower AUC(gluc), higher insulin sensitivity, and better β-cell function (all P ≤ 0.0097). CONCLUSIONS: Among women with a positive GCT, those tested in the afternoon have better metabolic function and a lower risk of GDM on subsequent OGTT.
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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 | 0.001 | 0.003 |
| 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".