Trajectories of care in congenital heart disease – the long arm of disease in the womb
Bibliographic record
Abstract
Our understanding of the development of congenital heart disease (CHD) across the lifespan has evolved. These include the evidence for the change in demographics of CHD, the observations that lifelong complications of CHD result in CHD as a lifespan disease, and the concept of long windows of exposure to risk that start in foetal life and magnify the expression of risk in adulthood. These observations set the stage for trajectories as an emerging construct to target health-service interventions. The lifelong cardiovascular and systemic complications of CHD make the long-term care of these patients challenging for cardiologists and internists alike. A life-course approach is thus required to facilitate our understanding of the natural history and to orient our clinical efforts. Three specific examples are illustrated: neurocognition; cancer resulting from exposure to low-dose ionizing radiation; and cardiovascular disease acquired in ageing adults. As patients grow, they do not just want to live longer, they want to live well. With the need to move beyond the mortality outcome, a shift in paradigm is needed. A life-course health development framework is developed for CHD. Trajectories are used as a complex construct to illustrate the patient's healthcare journey. There is a need to define disease trajectories, wellness trajectories and ageing trajectories in this population. Disease trajectories for repaired tetralogy of Fallot, transposition of the great arteries and the Fontan operation are hypothetically constructed. For clinicians, the life-course horizon helps to frame the patient's history and plan for the future. For researchers, life-course epidemiology offers a framework that will help increase the relevance of clinical enquiry and improve study design and analyses. A health-service policy framework is proposed for a growing number of conditions that start in the before birth and extend as long as contemporary survival now permits. Ultimately, the goal is the precision delivery of health services that enables lifelong health management, organization of developmental health services, and integration of vertical and horizontal health-service delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".