Evaluating geographic access to cardiac catheterization facilities to inform regionalized models of care for acute myocardial infarction
Bibliographic record
Abstract
Research has shown that regional acute myocardial infarction (AMI) care models reduce the time to treatment for percutaneous coronary intervention (PCI). By evaluating the areas with timely access it is possible to develop an evidence-based argument for the direct transfer of AMI patients in these areas to hospitals with specialized cardiac care. This program of research will show how applied geography using Geographic Information Systems (GIS), and administrative data can be used to describe current access to urgent, invasive, cardiac care in Canada and to understand the best practices when applying geographic tools to aid in health services planning through four sub-studies. Our results using administrative data suggest changing care over time and trends towards improved outcomes. These findings are in a context of clinical trial evidence in the published literature demonstrating the benefits of early procedural intervention for AMI patients. At the time of our study, we found that approximately 64% of the adult population had access to PCI across Canada, but that the proportion with access varied across provinces based on the distribution of the population and the number of existing facilities. This research also provided evidence that the addition of new facilities could increase the populations with access if placed appropriately. However, the modeling assumptions used for these estimates may not have been ideal for application in a Canadian context. The Canadian emergency medical services (EMS) on-scene intervals and response time intervals in rural areas were longer than those reported and incorporated from US studies. The application of the revised assumptions reduced the population proportion estimated to have access but still appropriately identified the larger areas with gaps in service. This program of research provides support that GIS is a valuable tool for evaluating those areas where regional care models could be implemented based on a specified pre-hospital time constraints. In light of our findings, we encourage researchers to use setting specific assumptions when possible and recognize the importance of understanding how the variations in modeling pre-hospital times can affect the estimated population access.
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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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".