The Early Origins of Cardiovascular Health and Disease: Who, When, and How
Bibliographic record
Abstract
Almost 30 years ago, a series of epidemiological studies popularized the early programming theory that had resulted from observed associations between low birthweight and increased cardiovascular morbidity and mortality later in life. Since then, several clinical and experimental models have been created to understand the principles and mechanisms of this fascinating phenomenon and describe its relevance to the pathophysiology of cardiovascular and many other chronic diseases. Despite the growing body of published evidence, the specific mechanisms mediating early programming effects are still elusive. Moreover, many controversial issues have arisen regarding the characteristics of the most commonly used clinical and experimental models, the existence of potential windows of susceptibility for different organs, and the presence of sex differences in its pathophysiology. Therefore, this review synthesizes some of the antecedents behind the early programming theory and discusses some of the controversial issues surrounding it. Early programming has been extensively linked to several chronic diseases; however, for the purposes of this review we have concentrated on the potential role of this entity in the pathophysiology of chronic cardiovascular diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".