Bibliographic record
Abstract
The instinctive concern of a pregnant woman for her baby creates a receptive environment for advice, and so it is particularly important that medical recommendations during pregnancy should be factual and reasoned. In this issue of Diabetes Care , Black et al. (1) have shown in a group of women who did not have gestational diabetes mellitus (GDM) by traditional thresholds that maternal overweight and obesity accounted for 21.6% of large-for-gestational-age (LGA) infants, and when GDM as defined by the newer American Diabetes Association (ADA) criteria was added into the equation, the combination was responsible for 23.3% of neonatal LGA. This article is a helpful addition to the debate balancing the impact of maternal adiposity or hyperglycemia on the risk of LGA in the baby. The historical poor outcomes of pregestational diabetes are testimony to the harmful effects of high glucose in early pregnancy as manifest by congenital malformations and in later pregnancy as evidenced by LGA and its consequences. Interestingly, over time with better glucose control the risk for congenital malformations has decreased but not the risk for LGA (2). A proportion of women with no known diabetes have a pancreas that cannot respond to the increased insulin requirements of pregnancy, and they therefore develop GDM. These women have more LGA and shoulder dystocia (3), and there is good evidence that treatment reduces these problems (4). Overweight mothers also have less favorable outcomes. In population studies, obesity is associated with more LGA, gestational hypertension, preeclampsia, GDM, and extra pounds retained postpartum, whereas excess weight gain during pregnancy is more closely associated with preeclampsia, LGA, and retained weight postpartum (5–7). Women who have bariatric …
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".