Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach
Bibliographic record
Abstract
BackgroundCongenital heart disease (CHD) is the most common major congenital anomaly, affecting approximately one in every 100 live births [1].Among congenital anomalies, 66% of preventable deaths are due to CHD, and 58% of the avertable morbidity and mortality due to congenital anomalies would result from scaling congenital heart surgery services [2].Every year, nearly 300,000 children and adults die from CHD, the majority of whom live in low-and middle-income countries (LMICs) [3].Approximately 49% of all individuals with CHD will require surgical or interventional care at some point in their lifetime [4]; as a result of advances in access to and the delivery of such services, over 95% of children born with CHD in high-income countries now live into adulthood [3].Here, adults have surpassed children in the number of CHD cases at a ratio of 2:1 [5].In contrast, in LMICs, over 90% of children born with CHD do not receive the care they need, often not surviving past their childhood [6].Indeed, even programs able to perform congenital heart surgery report limited resources, which are correlated with procedural volumes and complexity, and, thus, indirectly with long-term outcomes [7].The 8th World Congress of Pediatric Cardiology and Cardiac Surgery, which took place in Washington DC, United States in August 2023, was themed in large part around global gaps in CHD care.Here, a call to action on 'Addressing the Global Burden of Pediatric and Congenital Heart Diseases' was issued, which aligned with the 2030 Global Agenda for Sustainable Development and proposed 2030 goals for CHD-oriented capacity-building, data generation, and financing worldwide [8].This article gives an overview of the need for a health systems-oriented and lifespan perspective on CHD care.We discuss the advantages and disadvantages of vertical and horizontal approaches to CHD care and
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 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".