BORN to be validated: Assessing agreement between Ontario’s birth registry and CIHI-DAD
Bibliographic record
Abstract
BACKGROUND: The Better Outcomes Registry and Network Ontario Information System (BIS) has captured data on births in Ontario since 2012. Data and information quality is a foundational pillar of Ontario's birth registry. OBJECTIVE: To evaluate data quality and reliability, we compared birth data in the BIS with like data elements in the Canadian Institute for Health Information-Discharge-Abstract-Database (CIHI-DAD) which captures administrative, clinical, and demographic data on all hospital discharges. METHODS: We used unique pregnancy identifiers to deterministically link maternal records in the BIS to the CIHI-DAD in the fiscal years 2016-2017 to 2020-2021. Percent agreement and Cohen Kappa Coefficients (simple or weighted) with 95% confidence intervals (CI) assessed agreement on selected elements in both databases. Sensitivity analyses explored the impact of the COVID-19 pandemic on data entry and quality processes. RESULTS: There was excellent percentage agreement (⩾90%) between the two databases for all maternal elements assessed. Fourteen out of the twenty elements assessed indicated substantial (κ = 0.61-0.80) or almost perfect agreement (κ = 0.81-0.99) on Kappa tests. Sensitivity analyses restricting the linked cohort to data entered before (2016/2017-2019/2020) and during (2020/2021) the COVID-19 pandemic demonstrated no significant changes in agreement across all elements. CONCLUSION: Overall, the BIS and CIHI-DAD databases had high agreement on most maternal data elements; however, further examination is necessary to explore discrepancies identified.Implications for health information management practice:As the BIS is newer than the CIHI-DAD and uses a different method of data abstraction, routinely evaluating and enhancing data quality is crucial for providing accurate and valid evidence for health policy, surveillance, and research.
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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 | 0.111 | 0.241 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".