The Quebec Congenital Heart Disease Registry: A Model of Prospective Databank to Facilitate Research in Congenital Cardiology
Bibliographic record
Abstract
Background: A national registry of congenital heart disease (CHD) would facilitate project initiation, decrease costs, increase statistical power, and avoid duplication. Establishing such registries poses numerous challenges, but the current Canadian research ecosystem in CHD is well positioned to meet them. We assessed the feasibility of building a province-wide CHD registry by automatically identifying people with CHD and extracting their native cardiac anatomy from multiple clinical data sources, without the need for manual data entry. Methods: We designed a CHD registry of all fetuses and children with at least 1 echocardiographic report confirming CHD since 2000. We interfaced the registry with several clinical and echocardiography data sources from all paediatric cardiology programmes in Québec. Results: We extracted 885,287 echocardiogram reports and 70,121 clinical records. We identified CHD in 43,452 children and 4682 fetuses. There were 1128 (2.3%) cases with files in multiple institutions, and patients with more complex CHD were 3 times more likely to be seen in more than 1 institution. So far, the registry has been used to build and link CHD cohorts for 7 distinct projects. Conclusions: We demonstrated the feasibility of a baseline CHD registry in Québec without the need for manual data entry, in which other CHD research projects could be nested. This could serve as a blueprint to expand the registry and to develop an integrated approach where data gathered in caring for patients with CHD serve as data layers that incrementally contribute to a national cohort, for which data remain easily accessible and usable.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".