Adverse Childhood Experiences and the Cardiovascular Health of Children: A Cross-Sectional Study
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) encompass many possible traumatic and distressing experiences that occur in childhood. Such experiences include traumas such as abuse or neglect but may also include experiences of illness, injury, loss or separation, witnessing a serious event, experiencing a natural disaster and significant changes in the home environment. Research has identified an association between ACEs, such as abuse, household dysfunction, and poverty, and an increased likelihood of developing future health risk factors such as smoking, alcohol and drug use, physical inactivity, and obesity, as well as future chronic illnesses including cardiovascular, lung and liver diseases, and cancer which are, in part, related to these identified risk factors [1-3]. Work by Goodwin & Stein (2004), support these results showing that adults who had previously experienced childhood physical abuse, sexual abuse or neglect were 3.7 times more likely to develop cardiovascular disease (CVD) compared to others [4]. Stein and colleagues (2010) similarly showed that the accumulation of greater than three ACEs was associated with hypertension among adults [5]. Childhood factors including adverse events, socioeconomic status, illness, and growth patterns have also been linked to physiological differences in adult cardiovascular systems, accounting for 3.2% of variation of intima media thickness of the carotid artery in men and 2.2% variation in women [6]. Although this is a small effect, the fact that it remains significant after such a long latency period underscores its importance to cardiovascular health.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".