Long-Term Follow-Up on Morbidity Among Women With a History of Gestational Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) complicates up to 10% of pregnancies and is a well-known risk factor for type 2 diabetes mellitus (T2DM) and cardiovascular disease. Little is known about possible long-term risks of other diseases. BACKGROUND: The aim was to review the literature for evidence of associations with morbidity other than T2DM and cardiovascular disease and with long-term mortality. METHODS: A systematic review based on searches in Medline, Embase, and Cochrane Library until March 31, 2021, using a broad range of keywords. We extracted study characteristics and results on associations between GDM and disease occurrence at least 10 years postpartum, excluding studies on women with diabetes prior to pregnancy or only diabetes prior to outcome. The results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Newcastle-Ottawa Scale was used to assess risk of bias. RESULTS: We screened 3084 titles, 81 articles were assessed full-text, and 15 included in the review. The strongest evidence for an association was for kidney diseases, particularly in Black women. We found indication of an association with liver disease, possibly restricted to women with T2DM postpartum. The association between GDM and breast cancer had been studied extensively, but in most cases based on self-reported diagnosis and with conflicting results. Only sparse and inconsistent results were found for other cancers. No study on thyroid diseases was found, and no study reported on short-term or long-term mortality in women with a history of GDM. CONCLUSION: Given the frequency of GDM, there is a need for better evidence on possible long-term health consequences, in particular, studies based on comprehensive records of diagnosis of GDM and long-term health outcomes.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".