Cross-provincial use of cardiac services: the importance of data-sharing for clinical registries and outcomes research.
Bibliographic record
Abstract
BACKGROUND: The structure of the Canadian health care system lends itself to health services and health outcomes research. It is possible to track hospital admissions and discharges, physician billings and prescriptions using administrative databases. In addition, several provinces have developed registries that provide detailed clinical and procedural information. Using the unique personal health numbers assigned to all Canadian residents, linkage between administrative databases and population-based clinical registries provides important information regarding the use of health services and health outcomes. OBJECTIVE: To determine the extent of cross-border (British Columbia-Alberta border) use of cardiac services by British Columbia residents. METHODS: Population rates of cardiac procedures were calculated using two prospective clinical registries (British Columbia Cardiac Registries and Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease [APPROACH]), as well as administrative databases (the British Columbia Ministry of Health's hospitalization and Medical Services Plan databases). RESULTS: Analyses using only British Columbia data suggest low cardiac procedure rates for patients living in eastern British Columbia. By accessing APPROACH data, it was determined that more than 80% of British Columbia cardiac patients living along the British Columbia-Alberta border access procedural services in Alberta. CONCLUSIONS: While residents of eastern British Columbia appear to have reduced access to cardiac services when data from British Columbia are analyzed in isolation, they are actually accessing care in Alberta. Analyses based solely on single province data sources will underestimate cardiac procedures rates.
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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 | 0.153 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".