A meta-analysis of the prevalence of gestational diabetes in patients diagnosed with obstetrical cholestasis
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes and obstetrical cholestasis are common clinical conditions seen in clinical practice. There is evidence suggesting a coexisting relationship that could have a potential clinical implication related to stillbirth outcomes. OBJECTIVE: This study aimed to determine the prevalence of gestational diabetes in women with obstetrical cholestasis. STUDY DESIGN: A predefined protocol with a literature search was used to obtain all possible articles. A systematic review and meta-analysis of observational studies with quantifiable data published since 2010 were performed. Articles were evaluated and included in the study with specified criteria for the risk of bias using the Newcastle-Ottawa Scale. A meta-analysis was performed using Meta-analysis of Observational Studies in Epidemiology specifications to determine the prevalence of gestational diabetes in women with obstetrical cholestasis. RESULTS: A total of 16,748 patients with obstetrical cholestasis from 21 studies were included. The prevalence of gestational diabetes in women with obstetrical cholestasis was 13.9% (20 studies analyzed). Gestational diabetes was more common in women with obstetrical cholestasis than in women without obstetrical cholestasis (odds ratio, 2.129; 95% confidence interval, 1.697-2.670;10 studies). Gestational diabetes is twice more common in women with severe cholestasis than in women with mild cholestasis (odds ratio, 2.168; 95% confidence interval, 1.429-3.289; 4 studies). CONCLUSION: There is an increase in the prevalence of gestational diabetes among women diagnosed with obstetrical cholestasis. Compared with women with mild cholestasis, the increased risk of gestational diabetes in women with severe cholestatis is more than doubled. This suggests that the 2 conditions may have some biological similarities that affect clinical outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".