Non-communicable diseases: Opportunities to promote future health during the first 1000 day of life
Bibliographic record
Abstract
The Developmental Origins of Health and Disease (DOHaD) framework now underlies the evolution and epigenetics of many non-communicable diseases that develop in adult life. Type 2 diabetes, obesity, hypertension, heart disease and stroke in particular have links back to events during the first 1000 days of life, and as the world is witnessing an epidemic of these conditions, identifying measures able to contribute to reducing the potential for these NCDs to develop in our aging populations becomes all the more important. Parental health at conception and good maternal health and nutrition throughout pregnancy are known to be integral to normal infant development and health in later life, but more recently the central importance of infant nutrition that achieves healthy weight gain has become recognized. In this context, achieving growth patterns for infants that avoid either the onset of obesity or development of stunting during the first 1000 days of life appears to be an achievable goal with significant potential for the avoidance of many NCDs in later life. Hence the relevance of health promotion initiatives to share this knowledge among health care providers and educate parents on the benefits of optimal infant nutrition.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".