Diabetes in pregnancy among indigenous women in Australia, Canada, New Zealand and the United States: a systematic review of the evidence for screening in early pregnancy
Bibliographic record
Abstract
Recently proposed international guidelines for screening for gestational diabetes mellitus (GDM) recommend additional screening in early pregnancy for sub-populations at a high risk of type 2 diabetes mellitus (T2DM), such as indigenous women. However, there are criteria that should be met to ensure the benefits outweigh the risks of population-based screening. This review examines the published evidence for early screening for indigenous women as related to these criteria. Any publications were included that referred to diabetes in pregnancy among indigenous women in Australia, Canada, New Zealand and the United States (n = 145). The risk of bias was appraised. There is sufficient evidence describing the epidemiology of diabetes in pregnancy, demonstrating that it imposes a significant disease burden on indigenous women and their infants at birth and across the lifecourse (n = 120 studies). Women with pre-existing T2DM have a higher risk than women who develop GDM during pregnancy. However, there was insufficient evidence to address the remaining five criteria, including the following: understanding current screening practice and rates (n = 7); acceptability of GDM screening (n = 0); efficacy and cost of screening for GDM (n = 3); availability of effective treatment after diagnosis (n = 6); and effective systems for follow-up after pregnancy (n = 5). Given the impact of diabetes in pregnancy, particularly undiagnosed T2DM, GDM screening in early pregnancy offers potential benefits for indigenous women. However, researchers, policy makers and clinicians must work together with communities to develop effective strategies for implementation and minimizing the potential risks. Evidence of effective strategies for primary prevention, GDM treatment and follow-up after pregnancy are urgently needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".