Bibliographic record
Abstract
Abstract Epigenetics plays a role in many disorders that are present in the pediatric populations (eg, in early human development, including—embryologic and fetal life, infancy, childhood, and adolescence). Many syndromes were described because of their characteristic clinical features before epigenetic mechanisms were understood. Because time in development (age), as well as tissue specific and gender specific gene expression are so important in early development, it is not surprising that disturbances in control of gene expression will lead to pathologic conditions. These groups of disorders are important in pediatrics, and, thus, to the pediatrician. Mammalian flexibility in response to environmental change is now recognized to play a role in transgenerational programming for many chronic diseases of adults that appear to have their genesis in very early life. Working out the pathways and mechanisms for these disorders holds the promise for therapy in the future. Small for dates (IUGR) newborns should avoid excess weight gain during infancy and early childhood as this may help prevent some chronic diseases. Recording data on the placenta at birth is likely to be important for future research and possibly therapy. A new type of family history including multigenerational information on birth weights, environmental exposures, pregnancy histories, and exposure to various types of stress will be important for future medical care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".