Practice Patterns Among Healthcare Professionals for Screening, Diagnosis, and Management of Gestational Diabetes Mellitus (GDM) in Selected Countries of Asia, Africa, and Middle East
Bibliographic record
Abstract
Background: Healthcare professionals (HCPs) face several challenges while treating women with gestational diabetes mellitus (GDM) and often get confused by the different diagnostic criteria recommended by different scientific organizations. A survey was carried out to understand the practices of physicians and obstetricians in South Asia, Africa, and the Middle East, to identify the screening methods and diagnostic criteria used by them for managing women with GDM in the respective countries. Materials and Methods: HCPs across three different regions including South Asia, Middle East, and Africa were contacted through professional diabetes organizations. An online survey designed with Google Forms was created. The link to the survey was shared with HCPs, and the responses were collected and stored in the Google Sheets which was later downloaded for analysis. Results: A total of 356 doctors participated in the survey. The survey covered a total of 18 countries: 3 in South Asia, 5 in Africa, and 10 in the Middle East. The vast majority of the HCPs (64.6%) screened all pregnant women for GDM. About 42.4% of them screened for GDM between 24 and 28 weeks, 21.1% screened before 12 weeks, and the rest carried out screening at different time points. With regard to the screening method, 58.5% of the HCPs responded that they followed the two-step process. However, when asked about the criteria used, the responses were inconsistent. The criteria of the International Association of Diabetes in Pregnancy Study Group (IADPSG) were used by 36.5% doctors and the 1999 criteria by the old World Health Organization (WHO) were used by 27.2%, and only 23.9% reported following the American Diabetes Association (ADA) criteria. Conclusion: This large international survey shows that there are still considerable inaccuracies in doctors following the recommended guidelines for GDM diagnosis. This reiterates the fact that more education and training will help HCPs to manage GDM better.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".