An opportunity not to be missed – how do we improve postpartum screening rates for women with gestational diabetes?
Bibliographic record
Abstract
The ability to detect postpartum dysglycaemia, intervene and prevent type 2 diabetes in this high-risk population may be the most compelling reason to diagnose gestational diabetes. However, most studies show that less than 50% of women receive any glucose screening in the postpartum period and are thus denied this opportunity. Although many have advocated for simpler testing, the 75-g oral glucose tolerance test remains the gold standard as fasting glucose level will miss 30-40% of cases of type 2 diabetes and will not detect isolated impaired glucose tolerance. Haemoglobin A(1c) as a screening test has not been adequately studied. To improve postpartum screening rates, we need to increase awareness of the very high risk of type 2 diabetes, improve communication between providers, reduce fragmentation of care and introduce system factors that facilitate screening adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".