International survey on gestational diabetes
Bibliographic record
Abstract
OBJECTIVE: There is a lack of consensus among guidelines for screening, diagnosis and management of gestational diabetes (GDM). The purpose of this project was to determine current practices around GDM amongst members of the Medical Women's International Association (MWIA). METHODS: The MWIA with the Division of Endocrinology and Metabolism, University of Ottawa, developed an online survey using "Survey Monkey" and distributed it to its members. RESULTS: A total of 125 members completed the survey. Universal screening was recommended by 83% and most followed published guidelines. The 50 g glucose challenge test (GCT) was used for screening by 23% of participants while 25% recommended fasting blood glucose. There was also variability in how to proceed following a positive screening test. Almost 65 % recommended one of the glucose tolerance tests (50 g OGTT 26.7 % vs. 75 g OGTT 25.6% vs. 100 g OGTT 12.2%), while 18.8% recommended starting treatment and 16.7% used other diagnostic measures. Insulin was the most recommended treatment (75%) if diet/lifestyle failed. CONCLUSIONS: Our survey highlights the international variability that exists in the screening, diagnosis, and management of women with GDM. These differences impact on true prevalence rates and may underestimate the costs of this disease. The recommendation to move to a single internationally accepted diagnostic algorithm may be hampered by the variation in current practice globally.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".