Creating the BELgian COngenital heart disease database combining administrative and clinical data (BELCODAC): Rationale, design and methodology
Bibliographic record
Abstract
BACKGROUND: Congenital heart disease (CHD) entails a broad spectrum of malformations with various degrees of severity and prognosis. Consequently, new and specific healthcare needs are emerging, requiring responsive healthcare provision. Research on this matter is predominantly performed on population-based databases, to inform clinicians, researchers and policy-makers on health outcomes and economic burden of CHD. Most databases contain data either from administrative sources or from clinical systems. We describe the methodological design of the BELgian COngenital Heart Disease Database combining Administrative and Clinical data (BELCODAC), to investigate patients with CHD. METHODS: Data on clinical characteristics from three university hospitals in Belgium (Leuven, Ghent and Brussels) were merged with mortality and socio-economic data from the official Belgian statistical office (StatBel), and with healthcare use data from the InterMutualistic Agency, an overarching national organization that collects data from the seven sickness funds for all Belgian citizens. Over 60 variables with multiple entries over time are included in the database. RESULTS: BELCODAC contains data on 18,510 patients, of which 8926 patients (48%) have a mild, 7490 (41%) a moderately complex and 2094 (11%) a complex anatomical heart defect. The most prevalent diagnosis is Ventricular Septal Defect in 3879 patients (21%), followed by Atrial Septal Defect in 2565 patients (14%). CONCLUSIONS: BELCODAC comprises longitudinal data on patients with CHD in Belgium. This will help build evidence-based provision of care to the changing CHD population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".