Survey of Diabetologists and Obstetricians’ Practice Patterns Related to Care for Gestational Diabetes Mellitus During the COVID-19 Pandemic in India
Bibliographic record
Abstract
Aim: There are limited data on the management of gestational diabetes mellitus (GDM) during the coronavirus disease 2019 (COVID-19) pandemic. This survey was carried out in India to understand the practice patterns of diabetologists and obstetricians (OBs) during the pandemic. Materials and Methods: An online questionnaire was designed, and the link to the survey was shared with doctors through email. Questions were related to the diagnosis and management of GDM both before and during the COVID-19 pandemic. Results: A total of 117 diabetologists and 90 OBs from different parts of India participated in the survey. During the COVID-19 pandemic, diabetologists carried out higher random glucose and HbA1c tests and lower numbers of oral glucose tolerance tests (OGTTs), but differences compared with before COVID-19 were nonsignificant. The OBs reported doing a significantly lower number of OGTTs (85.6% vs. 95.6%, P = 0.02) and significantly more HbA1c tests (16.7% vs. 5.6%, P = 0.03) and self-monitoring of blood glucose (59.4% vs. 37.1%, P < 0.0001) during the pandemic, than earlier. Although 97% of all the doctors surveyed reported using some form of telemedicine, several challenges were identified. Conclusion: The COVID-19 pandemic has resulted in changes in the management of women with GDM. The use of digital technologies could help improve the care of women with GDM during such pandemics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".