Clinical Effectiveness of Cardiac Noninvasive Diagnostic Testing in Outpatients Evaluated for Stable Coronary Artery Disease
Bibliographic record
Abstract
Background Despite more than 4 million cardiac noninvasive diagnostic tests (NIT) being performed annually for stable coronary artery disease in the United States, it is unclear whether they are associated with downstream improvements in outcomes when compared with no testing. We sought to determine whether NIT was associated with reduced downstream major adverse cardiovascular events when compared with not testing. Methods and Results We conducted a population-based study of ≈1.5 million patients undergoing chest pain evaluation in Ontario, Canada. Patients were categorized into NIT and no-testing groups. Cause-specific proportional hazards models were used to compare the rate of major adverse cardiovascular events (composite outcome of unstable angina, acute myocardial infarction or cardiovascular mortality and each constituent) between the 2 groups after adjusting for clinically relevant covariates. The rate of the composite outcome was ≈25% lower for patients undergoing noninvasive testing (hazard ratio [HR], 0.77; 95% CI, 0.75-0.79). The benefits of testing were consistent for all 3 constituents of the composite; unstable angina (HR, 0.87; 95% CI, 0.82-0.93 for the NIT versus the no-testing group), myocardial infarction (HR, 0.83; 95% CI, 0.79-0.86 for the NIT versus the no-testing group) and cardiovascular mortality (HR, 0.68; 95% CI, 0.65-0.72 for the NIT versus the no-testing group). Conclusions Our large population-based study reports an ≈25% reduction in major adverse cardiovascular events that was independently associated with NIT in outpatients being evaluated for stable angina. This study demonstrates the prognostic importance of NIT versus no testing on the health of contemporary populations.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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, 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".