Screening Accuracy of the 50 g-Glucose Challenge Test in Twin Compared With Singleton Pregnancies
Bibliographic record
Abstract
CONTEXT: The optimal 50 g-glucose challenge test (GCT) cutoff for the diagnosis of gestational diabetes mellitus (GDM) in twin pregnancies is unknown. OBJECTIVE: This work aimed to explore the screening accuracy of the 50 g-GCT and its correlation with the risk of large for gestational age (LGA) newborn in twin compared to singleton pregnancies. A population-based retrospective cohort study (2007-2017) was conducted in Ontario, Canada. Participants included patients with a singleton (n = 546 892 [98.4%]) or twin (n = 8832 [1.6%]) birth who underwent screening for GDM using the 50 g-GCT. METHODS: We compared the screening accuracy, risk of GDM, and risk of LGA between twin and singleton pregnancies using various 50 g-GCT cutoffs. RESULTS: For any given 50 g-GCT result, the probability of GDM was higher (P = .0.007), whereas the probability of LGA was considerably lower in the twin compared with the singleton group, even when a twin-specific growth chart was used to diagnose LGA in the twin group (P < .001). The estimated false-positive rate (FPR) for GDM was higher in twin compared with singleton pregnancies irrespective of the 50 g-GCT cutoff used. The cutoff of 8.2 mmol/L (148 mg/dL) in twin pregnancies was associated with an estimated FPR (10.7%-11.1%) that was similar to the FPR associated with the cutoff of 7.8 mmol/L (140 mg/dL) in singleton pregnancies (10.8%). CONCLUSION: The screening performance of the 50 g-GCT for GDM and its correlation with LGA differ between twin and singleton pregnancies.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".