Prediction of insulin therapy in women with gestational diabetes: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVES: To identify antenatal risk factors that may predict the need for insulin treatment upon diagnosis of gestational diabetes (GDM), that is, to identify the specific characteristics of women diagnosed with GDM who did not achieve good glycemic control through lifestyle modifications. METHODS: We performed a comprehensive literature search in PubMed, Science Direct, Ebsco, and Scielo for studies evaluating the associations between antenatal factors and the need for insulin treatment published until January 28th, 2021. Random-effects models were used to estimate risk ratios and their 95% confidence interval. The quality of studies was assessed using the Newcastle-Ottawa Scale. Random-effects models were used to estimate outcomes, and effects reported as risk ratio and their 95% confidence interval. The systematic review and meta-analysis were registered in the International Prospective Register of Systematic Reviews. RESULTS: Eighteen observational studies were selected, reporting 14,951 women with GDM of whom 5,371 received insulin treatment. There were statistically significant associations between the need for insulin treatment and BMI ≥ 30 (RR:2.2; 95%CI: 1.44-3.41), family history of type 2 diabetes mellitus (RR:1.74; 95%CI: 1.56-1.93), prior personal history of GDM (RR:2.10; 95%CI: 1.56-2.82), glycated hemoglobin value at GDM diagnosis (RR:2.12; 95%CI: 1.77-2.54), and basal glycemia obtained in the diagnostic curve (RR: 1.2; 95%CI: 1.12-1.28). Nulliparity and maternal age were not determinants factor. There was moderate-to-high heterogeneity among the included studies. CONCLUSIONS: the strong causal association between BMI ≥ 30, family history of type 2 diabetes mellitus, prior history of GDM and glycosylated hemoglobin with the need for insulin treatment was revealed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".