Toward a Roadmap for Best Practices in Pediatric Preventive Cardiology: A Science Advisory From the American Heart Association
Bibliographic record
Abstract
Cardiovascular disease risk factors are highly prevalent among youth in the United States and Canada. Pediatric preventive cardiology programs have independently developed and proliferated to address cardiovascular risk factors in youth, but there is a general lack of clarity on best practices to optimize and sustain desired outcomes. We conducted surveys of pediatric cardiology division directors and pediatric preventive cardiology clinicians across the United States and Canada to describe the current landscape and perspectives on future directions for the field. We summarize the data and conclude with a call to action for various audiences who seek to improve cardiovascular health in youth, reduce the burden of premature cardiovascular disease, and increase healthy longevity. We call on heart centers, hospitals, payers, and policymakers to invest resources in the important work of pediatric preventive cardiology programs. We urge professional societies to advocate for pediatric preventive cardiology and provide opportunities for training and cross-pollination across programs. We encourage researchers to close evidence gaps. Last, we invite pediatric preventive cardiology clinicians to collaborate and innovate to advance the practice of pediatric preventive cardiology.
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 | 0.052 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.013 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".