The screening performance of glucose challenge test for gestational diabetes in twin pregnancies: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The screening accuracy of the 50 g-glucose challenge test (50 g-GCT) for gestational diabetes (GDM) has been described in singleton pregnancies. Given the physiologic differences and greater increase in insulin resistance in twin compared with singleton pregnancies, the performance of the 50 g-GCT in twin pregnancies may differ. OBJECTIVES: To perform a systematic review on the screening performance of the 50 g-GCT for gestational diabetes in twin pregnancies. DATA SOURCES: Ovid Medline, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL). STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTIONS: We included randomized controlled trials or cohort studies that evaluated the screening accuracy of the 50 g-GCT for GDM in twin pregnancies using the two-step approach. The primary outcome was the positive predictive value of the 50 g-GCT for GDM using the 140 mg/dL (7.8 mmol/L) threshold. STUDY APPRAISAL AND SYNTHESIS METHODS: Methodological quality of included studies was assessed using the QUADAS-2 tool. The positive predictive value (PPV) was pooled for studies that used similar test characteristics. RESULTS: =34.1%). The 50-g GCT screen positive rate in twin pregnancies was higher than that in singleton pregnancies. None of the studies performed routine OGTT. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: The PPV of 50 g-GCT for GDM in twin pregnancies when using a threshold of 140 mg/dL (7.8 mmol/L) is approximately 23%. There is currently no data on the sensitivity and specificity of the 50 g-GCT in twins.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".