Performance of guidelines for the screening and diagnosis of gestational diabetes mellitus during the COVID-19 pandemic: A scoping review of the guidelines and diagnostic studies evaluating the recommended testing strategies
Bibliographic record
Abstract
AIM: The COVID-19 pandemic has necessitated less resource-intensive testing guidelines to identify gestational diabetes mellitus (GDM). We performed a scoping review of the international evidence reporting the ability of diagnostic tests recommended during the pandemic to accurately identify patients with GDM, compared to pre-pandemic reference standards, and associated test and clinical outcomes. METHODS: June 2021. RESULTS: 145 unique citations were returned; after screening according to pre-specified inclusion criteria by title and abstract and then full text, 13 studies involving 40,836 pregnant people and an additional 52,884 instances of OGTT were included. Thresholds defined in the Australian pandemic guideline appear adequate to identify most GDM cases; false negative cases appeared at lower risk of hyperglycaemia-in-pregnancy(HIP)-related events. For UK and Canadian guidelines, a larger proportion would be misdiagnosed as non-GDM; these false negative cases had broadly equivalent HIP-related event rates as true positives. CONCLUSIONS: The OGTT remains the most effective test to identify abnormal glucose processing in pregnancy, supporting the prompt return to standard guidelines post-pandemic. Cohort studies investigating the impact of the change in guidelines on GDM pregnancies and associated outcomes are needed.
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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 | 0.030 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".