Achieving excellence in paediatric cardiac care in resource limited and resource plentiful settings and building successful care networks across different countries
Bibliographic record
Abstract
BACKGROUND: The delivery of paediatric cardiac care across the world occurs in settings with significant variability in available resources. Irrespective of the resources locally available, we must always strive to improve the quality of care we provide to our patients and simultaneously deliver such care in the most efficient and cost-effective manner. The development of cardiac networks is used widely to achieve these aims. METHODS: meeting of the Association for European Paediatric and Congenital Cardiology held in Dublin in April 2023. RESULTS: The three talks describe how centres of congenital cardiac excellence can be developed in low-income countries, middle-income countries, and well-resourced environments, and also reports how centres across different countries can come together to collaborate and deliver high-quality care. It is a fact that barriers to creating effective networks may arise from competition that may exist among programmes in unregulated and especially privatised health care environments. Nevertheless, reflecting on the creation of networks has important implications because collaboration between different centres can facilitate the maintenance of sustainable programmes of paediatric and congenital cardiac care. CONCLUSION: This article examines the delivery of paediatric and congenital cardiac care in resource limited environments, well-resourced environments, and within collaborative networks, with the hope that the lessons learned from these examples can be helpful to other institutions across the world. It is important to emphasise that irrespective of the differences in resources across different continents, the critical principles underlying provision of excellent care in different environments remain the same.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".