Epigenetics and Fetal Metabolic Programming: A Call for Integrated Research on Larger Cohorts
Bibliographic record
Abstract
Fetal metabolic programming is a concept firstsuggested by Barker and Hales (1,2) in the early1990s. On the basis of compelling epidemiologi-cal evidence, they hypothesized that fetal and perinatal events, such as maternal undernutrition, were central to determine one’s risk to develop chronic meta-bolic diseases. Such conditions, including obesity, di-abetes, and cardiovascular diseases, have become a very important population health concern. Since the first in-troduction of this concept, it has been corroborated by many animal but only few human studies (3). Therefore, a number of key issues remain to be clarified, with the most important being our knowledge of the mechanisms involved in fetal metabolic programming. In brief, more research is needed in human models of fetal metabolic programming. One such promising model is gestational diabetes mellitus (GDM). GDM is a form of diabetes first diagnosed during preg-
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 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".