Gestational diabetes: dilemma caused by multiple international diagnostic criteria
Bibliographic record
Abstract
AIMS: To highlight the variation in the diagnosis of gestational diabetes (GDM) as defined by six well-accepted international expert panels. METHODS: Two thousand, five hundred and fifty-four pregnant women underwent a 75-g oral glucose tolerance test for routine, antenatal GDM screening. They were classified using the criteria of the American Diabetes Association, Australasian Diabetes in Pregnancy Society, the Canadian Diabetes Association, the European Association for the Study of Diabetes, the New Zealand Society for the study of Diabetes and the World Health Organization (WHO). RESULTS: Between any two criteria, both the GDM prevalence (range; 7.9-24.9%) and the women classified differently [range; 70 (2.7%)-454 (17.8%) women], was significant (P<0.001). The most inclusive criteria, i.e. Australasian, despite generating the highest prevalence of GDM, did not pick up all the women identified by the most restrictive criteria, i.e. Canadian. The Australasian and the WHO criteria were associated with an increase in the number of Caesarean sections [odds ratio (OR); 1.64, 1.45, respectively] while the American, Canadian and New Zealand criteria identified an increase in macrosomia (birthweight>or=4000 g) incidence (OR; 2.09, 2.01, 1.92, respectively). CONCLUSIONS: The guidelines of the various professional committees, being based on consensus and expert opinion, show major discrepancies in their ability to identify women with GDM and their capacity to predict adverse pregnancy outcome. Only evidence-based criteria derived from reliable and consistent scientific data will eliminate the confusion caused in clinical practice.
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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 | 0.088 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".