Gestational diabetes: new criteria may triple the prevalence but effect on outcomes is unclear
Bibliographic record
Abstract
This article is part of a series on overdiagnosis looking at the risks and harms to patients of expanding definitions of disease and increasing use of new diagnostic technologies.Maternal obesity, excess maternal weight gain during pregnancy, and gestational diabetes are all associated with large for gestational age infants and other adverse outcomes.With obesity being a major risk factor for gestational diabetes (diabetes first recognised in pregnancy), the increasing incidence of the condition is unsurprising.Treatment of obesity during pregnancy has disappointingly little effect on the numbers of babies born large for gestational age, but treatment of gestational diabetes is more successful.This has led to an emphasis on diagnosing and treating gestational diabetes, but do the recently proposed diagnostic criteria 1 that triple its prevalence make sense?Is it good clinical care, or yet another example of overdiagnosis?A label of gestational diabetes brings with it an intervention package that includes glucose monitoring; extra clinic visits; more obstetric monitoring with greater likelihood of labour induction, operative delivery, and admission of the baby to special care; and, finally, for the mother, a label of high risk for diabetes.We argue that the diagnostic changes are unjustified because they are based on the results of an observational study and use a test that has poor reproducibility.Furthermore, there is no evidence of any treatment benefit from interventional studies.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".