BORN to be validated: Assessing agreement between Ontario’s birth registry and CIHI-DAD
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it