Gestational diabetes mellitus
Bibliographic record
Abstract
It gives us great pleasure to write this editorial for the special issue on gestational diabetes mellitus (GDM). The Journal of Diabetology (JOD) has been in existence for several years, and this is the first time that a special issue is being brought out by JOD. The fact that the first special issue is devoted to GDM shows the importance of the subject. We tried the special issue as an experiment as JOD is still a very new journal. We were overwhelmed with the number of articles received for this special issue from several countries, including India, Sri Lanka, Pakistan, Bangladesh, UAE, and the International Diabetes Federation (IDF) Middle East and North Africa (MENA) region and the United Kingdom. A range of articles included in this review include screening, diagnosis, and management of GDM, survey of diabetologist and obstetrician practice patterns, postpartum abnormal glucose tolerance and predictive factors, pregnancy outcomes, guidelines for physical activity and exercise, medical nutrition therapy in GDM, prevalence of thyroid dysfunction in GDM, and to top it all, exhaustive IDF MENA region guidelines for the management of hyperglycemia in pregnancy.[1] Another highlight of this issue is the article on the current screening methods and diagnostic criteria used for managing GDM in selected countries of Asia, Africa, and Middle East.[2] This special issue makes one of the important milestones in the publication of JOD, and we are happy that all these contributors have chosen JOD to publish their articles. We are sure everyone interested in GDM, including postgraduate students, physicians, diabetologists, and obstetricians, will find this to be of great interest, and this issue will become a collector's item. GDM has become a huge problem in Asia, particularly in South Asia and in the Middle East. The prevalence of GDM generally parallels the prevalence of prediabetes or diabetes in the community. About two decades ago, the prevalence of GDM in India was less than 5%.[3,4,5] Today, in the urban areas of several countries in Asia, including India, the prevalence of GDM has increased, ranging anywhere between 15% and 35%[6,7,8] depending on the diagnostic criteria used. GDM is also an area where there are lot of controversies with regard to its screening, diagnosis, and management. With respect to low-resource settings in countries like India, there are critical gaps with regard to the best practice of care that can be provided to women with GDM. To address these gaps, aligning with the recommendations of the IDF's Global Call to Action and Policy Brief on Diabetes in Pregnancy, the Women in India with GDM Strategy (WINGS) study was formed,[9] which developed a model of care for women with GDM for implementation in low-resource settings. WINGS MODEL OF CARE Through the WINGS project, a low-cost mode of care was developed under which women with GDM were recruited and followed up throughout their pregnancy.[10] Health care professionals who collaborated in the project were trained under the model of care.[11] Women with GDM were also educated about the impact of GDM on their health and babies through face-to-face counseling with nutritionists and health care professionals.[12] Several manuals were published as part of the training programme that was conducted for the health care professionals including diabetologists, obstetricians/gynaecologist, nurses, and dietitians.[13] Women with GDM were also provided with educational booklets to track and monitor their diet and exercise.[12] Some of the important outcomes of the WINGS project include the following: The model of care was found to be effective in reducing adverse outcomes in women with GDM, to the levels similar to women with normoglycemia.[14] One other important result from the project was the 95.8% postpartum follow-up rate that was achieved.[15] The implementation of Model of Care (MOC) showed significant improvement in the physical activity levels and a decrease in sedentary behavior and an increase in daily step count. These changes were associated with improved glycemic control and reduction in adverse neonatal outcomes.[16] The WINGS model of care, therefore, proved to be successful in terms of screening, management, and follow-up of women with GDM in India. Creating guidelines and protocols will help manage GDM even in low-resource settings. In this context, it is useful to have guidelines for GDM specific for different regions of the world.[1] Thus, we are sure the special articles on medical nutrition therapy, physical activity, and the IDF MENA guidelines would be of great interest to physicians, obstetricians, and gynecologists. We would welcome feedback from our readers regarding this special issue on GDM. We would also be delighted to receive suggestions regarding topics for future special issues of JOD. It has been a great pleasure to work with the distinguished contributors to this issue and a big thank you to them for responding to our invitation to contribute to this special issue and for submitting their articles on time. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".