Relation between initial treatment strategy in stable coronary artery disease and 1-year costs in Ontario: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease is costly, and annual expenditures are projected to increase. Our objective was to examine the variation in patient-level costs and identify drivers of cost in patients with stable coronary artery disease. METHODS: In this retrospective cohort study using administrative databases in Ontario, Canada, we identified all patients with stable coronary artery disease after index angiography between Oct. 1, 2008, and Sept. 30, 2011. We excluded patients with a myocardial infarction within 90 days before the index, with normal coronaries, or with mild coronary disease. We categorized hospitals into low, medium or high revascularization ratio centres. The primary outcome was cumulative 1-year health care costs. A hierarchical generalized linear model identified patient, physician and hospital characteristics associated with patient costs, with 2 main covariates of interest: treatment allocation (medical v. percutaneous coronary intervention v. coronary artery bypass grafting) and hospital revascularization ratio. RESULTS: A total of 183 630 angiography procedures were performed in Ontario during the study period. The final cohort included 39 126 patients with stable coronary artery disease, of which 15 138 received medical treatment and 23 988 received revascularization. The mean 1-year cost was $24 026 (interquartile range $8235-$30 511). The mean costs for medical management and revascularization were $18 069 and $27 786, respectively. The strongest predictor of costs was revascularization (percutaneous coronary intervention: cost ratio 1.27, 95% CI [confidence interval] 1.24-1.31; coronary artery bypass grafting: cost ratio 2.62, 95% CI 2.53-2.71). Hospital revascularization ratio did not significantly affect costs. There was no significant interaction between treatment and revascularization ratio. INTERPRETATION: Most health care costs were due to acute care hospital admissions, and costs were higher for patients undergoing revascularization than medical therapy. This study suggests that treatment decision has a substantial impact on health care resources.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".