The Glucose Challenge Test in Pregnancy Identifies Future Risk of Diabetes
Bibliographic record
Abstract
CONTEXT: Women with gestational diabetes (GDM) have an increased future risk of type 2 diabetes but, in practice, their recommended postpartum glucose tolerance testing is often missed or substituted with measurement of A1c instead. OBJECTIVE: We hypothesized that the antenatal screening glucose challenge test (GCT) should predict future diabetes risk and, if so, would have thresholds that identify the same degree of risk as the diagnosis of prediabetes on postpartum measurement of A1c. METHODS: With population-based administrative databases, we identified all women in Ontario, Canada, who had a GCT in pregnancy with delivery between January 2007 and December 2017, followed by measurement of A1c and fasting glucose within 2 years postpartum (n = 141 858, including 19 034 with GDM). Women were followed over a median of 3.5 years for the development of diabetes. RESULTS: Under the assumption of a linear exposure effect, the 1-hour post-challenge glucose concentration on the GCT was associated with an increased likelihood of developing diabetes (hazard ratio 1.39; 95% CI, 1.38-1.40). A GCT threshold of 8.0 mmol/L predicted the same 5-year risk of diabetes (6.0%; 95% CI, 5.8-6.2) as postpartum A1c 5.7% (identifying prediabetes). Moreover, in women with GDM, a GCT threshold of 9.8 mmol/L equaled prediabetes on postpartum A1c in predicting a 5-year risk of diabetes of 16.5% (14.8-18.2). CONCLUSION: The GCT offers predictive capacity for future diabetes in pregnant women. In women with GDM, this insight could identify those at highest risk of diabetes, toward whom postpartum screening efforts should be most strongly directed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".