Pragmatic trials can address diagnostic controversies: recent lessons from gestational diabetes
Bibliographic record
Abstract
OBJECTIVE: The aim of the paper is to discuss how a pragmatic definition could change our conception of diagnosis, using gestational diabetes mellitus (GDM) as an example. STUDY DESIGN: We review the diagnostic controversy that followed an observational study showing a linear relationship between maternal glycaemia and adverse pregnancy outcomes and the resolution proposed 15 years later by a recent pragmatic trial comparing two screening approaches (one- vs two-step) with different diagnostic thresholds. RESULTS: The pragmatic trial involved approximately 24,000 women. The one-step screening strategy using lower GDM thresholds diagnosed twice as many women with GDM, but pregnancy outcomes were not different. We examine how the pragmatic approach integrates research into practice and defines the meaning of a diagnosis according to patient outcomes. The approach is ethically and scientifically sound as compared to the previous methodology, where observational research separated from care gave a theoretical definition of GDM that may have misled medical practice for two decades. CONCLUSION: Pragmatic research integrated into practice can revolutionize our conception of medical diagnosis in the best medical interest of patients.
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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 | 0.117 | 0.288 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".