Population rates of cardiac catheterization and yield of high-risk coronary artery disease
Bibliographic record
Abstract
BACKGROUND: The optimal population rate of cardiac catheterization is unknown. One potential way to determine it would be to examine whether there is a population rate beyond which the yield of high-risk coronary artery disease (CAD) does not rise. METHODS: Using a detailed clinical registry that captures all patients undergoing cardiac catheterization in Alberta, we determined annual population rates of cardiac catheterization and the corresponding yield of cases of high-risk CAD in each of Alberta's 17 health regions from 1995 to 2002. Least squares linear regression analysis and hierarchical modelling methods were then used to assess the linear relation between catheterization rates and rates of high-risk CAD. RESULTS: The age-adjusted average rate of cardiac catheterization among men ranged from 404.9 to 638.1 per 100,000 population aged over 20 years. Among women, the average rate ranged from 171.8 to 314.0 per 100,000. For both sexes, increased regional rates of catheterization were associated with a linearly increasing yield of high-risk CAD, with no evidence of a plateau in yield when more procedures were performed. One additional case of high-risk CAD was identified for every 2.5 additional cardiac catheterization procedures performed among men, and for every 3.7 additional procedures performed among women. INTERPRETATION: The increasing yield of patients with high-risk CAD associated with increased regional population rates of cardiac catheterization, together with the absence of a plateau in yield, suggests that Alberta's population rates of cardiac catheterization are suboptimal to detect people with high-risk CAD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, 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".