A Meta-Analysis on the Prevalence and Risk of Gestational Diabetes Mellitus in the Context of COVID-19
Bibliographic record
Abstract
Background: The objectives of this study are: 1) to compare the prevalence of gestational diabetes mellitus (GDM) among pandemic and pre-pandemic cohorts, 2) to evaluate the risk of GDM among pregnant women who tested positive and negative for coronavirus disease 2019 (COVID-19), and 3) to evaluate the risk of COVID-19 among pregnant women diagnosed with and without GDM. Methods: A literature search was carried out in PubMed and Cochrane library databases with relevant keywords from its inception till March 2024. Observational studies that 1) evaluated the prevalence of GDM during pandemic and pre-pandemic period, and 2) investigated the GDM and COVID-19 status among pregnant women were included. Results: The analysis revealed that the prevalence of GDM was significantly increased by 17% (odds ratio (OR), 1.17; 95% confidence interval (CI), 1.12 to 1.23; P < 0.00001) during the pandemic period compared to pre-pandemic period and the odds of pregnant women with GDM tested positive for COVID-19 were 1.28-fold greater (OR, 1.28; 95% CI, 1.13 to 1.44; P < 0.0001) than the odds of pregnant women with GDM tested negative for COVID-19. However, the analysis also revealed that pregnant women with COVID-19 were less likely (OR, 0.02; 95% CI, 0.01 to 0.02; P < 0.00001) to be diagnosed with GDM when compared to pregnant women with COVID-19 and without GDM. Conclusion: The present study suggests that GDM acts as a risk factor for COVID-19 infection among pregnant women. This might be due to the hypothesis that altered sense of taste is associated among pregnant women with GDM and COVID-19 due to the taste receptor polymorphisms which regulates the innate immunity downstream signaling. However, molecular studies were needed to validate this hypothesis and evaluate the therapeutic role of taste receptors in the management of COVID-19 and GDM. J Endocrinol Metab. 2024;14(5):226-239 doi: https://doi.org/10.14740/jem1020
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".