A meta-analysis of the prevalence of gestational diabetes in patients diagnosed with obstetric cholestasis.
Bibliographic record
Abstract
Objective To determine the prevalence of gestational diabetes in women with obstetric cholestasis. Design Systematic review and meta-analysis of data published since 2010. Selection Observation studies with quantifiable data. Sample A total of 16748 patients with obstetric cholestasis from 21 studies were included. Methods A pre-defined protocol with extensive literature search plus other sources to obtain all possible articles was followed. All articles were evaluated and included in the study with specified criteria for the risk of bias using the Newcastle Ottawa Score. A meta-analysis was performed with MOOSE specifications met. Main Outcome measure Prevalence of gestational diabetes in the obstetric cholestasis population. Results The prevalence of gestational diabetes in the obstetric cholestasis population was 13.9% (20 studies analysed). Gestational diabetes was seen more in the obstetric cholestasis group compared to the non-obstetric group (OR 2.129, 95%CI, 1.697 to 2.670,10 studies). Severe cholestasis has more gestational diabetes cases (OR 2.168, 95% CI, 1.429 to 3.289, 4 studies) compared to mild cholestasis. Conclusion There is a significant co-relation of gestational diabetes among the diagnosed obstetric cholestasis population. With a diagnosis of obstetric cholestasis is made, it is necessary to test for undiagnosed gestational diabetes further. This approach may reduce the risk of stillbirth in severe obstetric cholestasis cases. PROSPERO Registration number CRD42021223886
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.059 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".