Reducing Rates and Risk of Stroke in Adults With Congenital Heart Disease: What Can We Do Now, and What Should We Do Next?
Bibliographic record
Abstract
ongenital heart disease (CHD) is the most common birth defect, affecting approximately 1% of all live births. 1 Importantly, due to medical and surgical advances over the past several decades, it is also now the fastest-growing area of adult cardiology.Even for complex congenital heart lesions, which comprise approximately 10% of CHD, survival well into adulthood is now commonplace, and adults with CHD now outnumber children living with CHD. 2 See Article by Sodhi-Berry et al.A longer lifespan with CHD, however, is also associated with a longer exposure to health risks, both later-stage CHD-related complications as well as general aging-related conditions, 3 and potentially in synergy.4 In particular, strategies to optimize brain health in this population remain a priority for further research.People with CHD may experience baseline susceptibilities to their brain health from neurodevelopmental and acquired insults beginning in early life 5 and may be at increased risk for dementia, particularly earlieronset disease.6 For individuals with CHD, who have an overall increased risk for stroke, it is therefore critical to have a better understanding as to which individuals with CHD are at highest risk, when in their lives they are most susceptible, and why.Neurocognitive trajectories in people with CHD in particular may be affected by stroke, which in the general population is the leading cause of adult-acquired disability and a major risk factor for cognitive impairment and dementia.7 In this issue of the Journal of the American Heart Association (JAHA), Sodhi-Berry and colleagues describe incidence and risk factors for ischemic and hemorrhagic stroke in adults with CHD (ACHD) using administrative data from Western Australia from 2000 to 2017.8 The authors calculated estimates for ageand sex-stratified incidence rate ratios for stroke in ACHD versus the general population and assessed ACHD-specific risk factors for stroke using a nested case-control design.The study contributes additional knowledge regarding rates of stroke in ACHD, described in only a handful of population-based studies to date.It also contributes data from Australasia; previous estimates are from North American and Northern European data.[9][10][11] Information capturing Indigeneity status and rurality are additional strengths.Identifying CHD within administrative data can be a challenge in and of itself due to misclassification alongside codes for acquired cardiac disease.12 The authors used methods that were previously validated outside of Australia, but
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.029 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".