Clinical Population about Diabetes during Pregnancy: A Systematic Literature Review
Bibliographic record
Abstract
Diabetes during pregnancy, including gestational diabetes mellitus (GDM) and pre-existing diabetes, presents significant challenges to maternal and fetal health. Clinical populations encompass diverse pregnant individuals, each with unique risk factors and outcomes related to diabetes in pregnancy. This systematic literature review aimed to synthesize findings from eligible studies conducted between January 1, 2020, and December 30, 2023, sourced from Web of Science, PubMed, Medline, and the Cochrane Database of literature Reviews, to comprehensively examine diabetes during pregnancy within clinical populations. We followed established systematic review methodologies, including study selection, data extraction, and analysis. Eligible studies underwent rigorous screening to ensure relevance and quality. Data were systematically extracted to identify trends and patterns in epidemiology, risk factors, clinical management, and outcomes. Among the 15 eligible studies, our analysis revealed variations in the prevalence of diabetes during pregnancy across clinical populations, ranging from 5% to 15%. Socioeconomic factors, ethnicity, and maternal age were significant risk factors. Clinical management strategies varied, with insulin therapy predominant in pre-existing diabetes cases (68%) and dietary interventions in GDM (45%). Fetal macrosomia occurred in 18% of cases, while neonatal hypoglycemia affected 14% of infants born to mothers with diabetes. This systematic literature review highlights the multifaceted nature of diabetes during pregnancy in clinical populations. Variations in prevalence and risk factors underscore the importance of tailored healthcare interventions. Diverse management approaches necessitate individualized care plans. The prevalence of adverse outcomes necessitates vigilant monitoring and timely interventions. Our findings inform evidence-based practices research priorities, and support improved care for pregnant individuals with diabetes in clinical populations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".