Systematic Review of risk score prediction models using maternal characteristics with and without biomarkers for the prediction of GDM
Bibliographic record
Abstract
Abstract Background GDM is associated with adverse maternal and fetal complications. By the time GDM is diagnosed, continuous exposure to the hyperglycaemic intrauterine environment can adversely affect the fetus. Hence, early pregnancy prediction of GDM is important. Aim To systematically evaluate whether composite risk score prediction models can accurately predict GDM in early pregnancy. Method Systematic review of observational studies involving pregnant women of <20 weeks of gestation was carried out. The search involved various databases, grey literature, and reference lists till August 2022. The primary outcome was the predictive performance of the models in terms of the AUC, for <14 weeks and 14-20 weeks of gestation. Results Sixty-seven articles for <14 weeks and 22 for 14-20 weeks of gestation were included (initial search - 4542). The sample size ranged from 42 to 1,160,933. The studies were from Canada, USA, UK, Europe, Israel, Iran, China, Taiwan, South Korea, South Africa, Australia, Singapore, and Thailand. For <14 weeks, the AUC ranges were 0.59-0.88 and 0.53-0.95, respectively for models that used only maternal characteristics and for those that included biomarkers. For 14-20 weeks these AUCs were 0.68-0.71 and 0.65-0.92. Age, ethnicity, BMI, family history of diabetes, and prior GDM were the 5 most commonly used risk factors. The addition of systolic BP improved performance in some models. Triglycerides, PAPP-A, and lipocalin- 2, combined with maternal characteristics, have the highest predictive performance. AUC varied according to the population studied. Pooled analyses were not done due to high heterogeneity. Conclusion Accurate GDM risk prediction may be possible if common risk factors are combined with biomarkers. However, more research is needed in populations of high GDM risk. Artificial Intelligence-based risk prediction models that incorporate fetal biometry data may improve accuracy.
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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 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".