Innovation through collaboration: Identifying opportunities to improve congenital anomalies surveillance in Canada
Bibliographic record
Abstract
SETTING: The burden of congenital anomalies is a significant public health concern. In response to the World Health Organization's recommendations, Canada developed and strengthened congenital anomalies surveillance to build capacity for prevention and optimal health outcomes. Historically, the Public Health Agency of Canada (PHAC) exclusively used hospital discharge data for the Canadian Congenital Anomalies Surveillance System (CCASS). A primary objective of the CCASS is to report prevalence, trends, and factors associated with congenital anomalies in Canada. However, the purpose of hospital discharge data is not for congenital anomalies surveillance; therefore, enhanced local data, which have more complete case ascertainment and additional data quality measures, are necessary. INTERVENTION: Recognizing these significant limitations, PHAC, the provincial and territorial governments, physicians, public health practitioners, and academics collaborated on a project to enhance the CCASS with regional data and expertise. Subsequently, the Government of Canada InfoBase platform will use this enhanced dataset for national reporting. OUTCOMES: We developed standardized case definitions, a data submission form, and data quality tools, and surveyed programs to describe local congenital anomalies surveillance practice, and to identify barriers and facilitators that impact congenital anomalies surveillance efforts. IMPLICATIONS: This synergistic collaboration across jurisdictions, disciplines, and health care sectors is essential to support Canada's enhanced congenital anomalies surveillance. We identified common themes on funding, operational requirements, data standardization, and legal and privacy considerations from the survey. These themes can be used to inform policy and decision-makers for sustainable congenital anomalies surveillance and to amplify the current momentum.
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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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".