Internal and External Data Linkage of Complex Relational Database: Results from CorHealth Ontario
Bibliographic record
Abstract
IntroductionCorHealth Ontario, formerly Cardiac Care Network (CCN), maintains a registry of patients undergoing select cardiac procedures/surgeries in Ontario, Canada. This population-based database contains over 35 datasets with complex structure, linked by unique primary key or multiple keys. Objectives and ApproachWe aimed to simplify the complex CorHealth database so that research analysts could create study cohorts more efficiently and effectively, and to enrich the study cohort by getting more clinical information through database linkage. Through internal linkage, we could combine clinical fields from multiple CorHealth datasets. While the CorHealth dataset may not have all the clinical information needed for a given study, we may link the CorHealth study cohort externally to other administration databases to obtain additional fields via the probability matching (i.e., identical patient ID, hospital ID and procedure/surgery date). ResultsAfter identifying the primary keys on the relational database flowchart, we designed new data structures by combining similar topic datasets. The total number of datasets was reduced from 35 to 13. This simplified CorHealth dataset includes one main CorHealth dataset (including demographic information, referral data, comorbidities) plus 12 other linkable specific datasets (including stent, vessel, TAVI, STEMI). Through internal linkage, we can get the stent numbers, lengths and types of Percutaneous Coronary Interventions from the Stent dataset. Linking to Discharge Abstract Database (DAD), we can get the hospital length of stay and the episode of care of hospital transfer for each procedure; linking to The Ontario Health Insurance Plan database (OHIP), we can find the graft numbers and vessel types of Coronary Artery Bypass Graft. Conclusion/ImplicationsTo improve the research capacity and increase the value of the CorHealth database, analysts could create enhanced cardiovascular study cohorts derived from the simplified CorHealth database, plus internal linkage from other CorHealth datasets, and external data linkage from population-based administrative sources. We have accomplished three reports (PCI/CABG/TAVI) accordingly in 2017/18.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".