Commentary: The developmental origins of health and disease: an appreciation of the life and work of Professor David J.P. Barker, 1938-2013
Bibliographic record
Abstract
David J.P. Barker was a physician, a biologist and one of the most influential epidemiologists of our time. His ‘foetal programming hypothesis’ (‘Barker hypothesis’) transformed our thinking about the causes of diabetes, cardiovascular disease and cancer. He challenged the idea that they are explained by bad genes and unhealthy adult lifestyles, and proposed that their roots lie in the early life environment: ‘The nourishment a baby receives from its mother, and its exposure to infection after birth, determine its susceptibility to chronic disease in later life’.1,2 By permanently ‘programming’ the body’s metabolism and growth, they determine the pathologies of old age. His initially controversial, but now widely accepted, ideas have stimulated an explosion of research worldwide into early development and later disease (‘developmental origins of health and disease’ or DOHaD). David thought that ‘the poorer health of people in lower socio-economic groups or living in impoverished places is linked to neglect of the welfare of mothers and babies’. He argued that to pull back the modern epidemics of chronic disease we should prioritize the health and nutrition of girls, pregnant women and infants.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.054 | 0.063 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".