Gestational diabetes mellitus and COVID-19: The epidemic during the pandemic
Bibliographic record
Abstract
During the global coronavirus disease 2019 (COVID-19) pandemic, people worldwide have experienced an unprecedented rise in psychological distress and anxiety. In addition to this challenging situation, the prevalence of diabetes mellitus (DM), a hidden epidemic, has been steadily increasing in recent years. Lower-middle-income countries have faced significant barriers in providing accessible prenatal care and promoting a healthy diet for pregnant women, and the pandemic has made these challenges even more difficult to overcome. Pregnant women are at a higher risk of developing complications such as hyper-tension, preeclampsia, and gestational diabetes, all of which can have adverse implications for both maternal and fetal health. The occurrence of gestational diabetes has been on the rise, and it is possible that the pandemic has worsened its prevalence. Although data is limited, studies conducted in Italy and Canada suggest that the pandemic has had an impact on gestational diabetes rates, especially among women in their first trimester of pregnancy. The significant disruptions to daily routines caused by the pandemic, such as limited exercise options, indicate a possible link between COVID-19 and an increased likelihood of experiencing higher levels of weight gain during pregnancy. Notably, individuals in the United States with singleton pregnancies are at a significantly higher risk of excessive gestational weight gain, making this association particularly important to consider. Although comprehensive data is currently lacking, it is important for clinical researchers to explore the possibility of establishing correlations between the stress experienced during the pandemic, its consequences such as gestational gain weight, and the increasing incidence of gestational DM. This knowledge would contribute to better preventive measures and support for pregnant individuals during challenging times.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".