Usefulness of cut‐off points of International criteria for prediction of post‐partum diabetes and prediabetes among Chinese women with gestational diabetes
Bibliographic record
Abstract
AIMS: This study tests whether cut-off points of the International Association of Diabetes and Pregnancy Study Group's (IADPSG) criteria had threshold effects on post-partum prediabetes and diabetes among Chinese pregnant women with gestational diabetes mellitus (GDM). MATERIALS AND METHODS: A total of 507 out of 1000 women with GDM (948 of them enrolled in a lifestyle trial during pregnancy) turned up for the follow-up study and underwent a 75-g 2-h oral glucose tolerance test. GDM was diagnosed based on the IADPSG's criteria while post-partum diabetes and prediabetes were defined by the World Health Organization's. Generalized logit model was used to obtain odds ratios (OR) and 95% confidence interval (CI) of fasting, 1-h and 2-h plasma glucoses (PGs) for post-partum diabetes and prediabetes. Restricted cubic spline was used to identify any threshold effects. RESULTS: At a median of 9.1 weeks post-partum, 3.7% (n = 19) women developed post-partum diabetes and 35.1% (n = 178) developed post-partum prediabetes. Fasting PG ≥ 5.1 mmol/L was associated with markedly increased risk of post-partum diabetes without a discernible threshold (adjusted OR: 3.87, 95% CI: 1.03-14.52) while 2-h PG ≥ 8.5 and ≥ 9.0 mmol/L had threshold effects on post-partum prediabetes (2.10, 1.33-3.30) and diabetes (4.02, 1.04-15.56). The 1-h PG also had a threshold at ≥10.0 mmol/L for prediabetes (1.67, 1.06-2.64), but it was not significant for post-partum diabetes. CONCLUSIONS: Among Chinese women with GDM, fasting PG ≥ 5.1 mmol/L was associated with post-partum diabetes without any discernible threshold effects while 2-h PG ≥ 8.5 and ≥ 9.0 mmol/L respectively identified women at high risk of post-partum prediabetes and diabetes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".