Bibliographic record
Abstract
Pregnancy poses a unique physiological challenge to the pancreatic β-cells. For normal glucose tolerance to be maintained in the setting of the insulin resistance of late gestation, the β-cells must markedly increase their secretion of insulin. It is believed that this enhanced secretion is achieved through the expansion of β-cell mass in response to circulating factors, including prolactin and placental lactogens (1,2). Conversely, failure of this compensatory response will result in maternal hyperglycemia or gestational diabetes mellitus (GDM) (3). However, our understanding of the mechanisms underlying normal islet adaptation in pregnancy and its failure in GDM remains limited at this time. In this context, the importance of elucidating the biology of this adaptive response is underscored by the novel insight that it could provide into the pathophysiology of β-cell dysfunction, with implications for not only GDM but also subsequent type 2 diabetes (1,2). Adiponectin is an adipocyte-derived hormone with pleiotropic effects on a broad array of physiological processes including energy homeostasis, vascular function, systemic inflammation, and cell growth (4,5). Most notably, it has emerged as an antidiabetic adipokine, with low serum adiponectin shown to predict incident type 2 diabetes in several populations (6). Similarly, hypoadiponectinemia in early pregnancy, or even prior to gestation, can predict the subsequent development of GDM in the second or third trimester (7). These antidiabetic associations have …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".