Impact of System and Physician Factors on the Detection of Obstructive Coronary Disease With Diagnostic Angiography in Stable Ischemic Heart Disease
Bibliographic record
Abstract
BACKGROUND: Wide variation exists in the detection rate of obstructive coronary artery disease (CAD) with elective coronary angiography for suspected stable ischemic heart disease. We sought to understand the incremental impact of nonclinical factors on this variation. METHODS AND RESULTS: We included all patients who underwent coronary angiography for possible suspected stable ischemic heart disease, from October 1, 2008, to September 30, 2011, in Ontario, Canada. Nonclinical factors of interest included physician self-referral for angiography, the physician type (invasive or interventional), and hospital type. Hospitals were categorized into diagnostic angiogram only centers, stand-alone percutaneous coronary intervention centers, or full service centers with coronary artery bypass surgery available. Multivariable hierarchical logistic models were developed to identify system and physician-level predictors of obstructive CAD, after adjustment for patient factors. Our cohort consisted of 60 986 patients, of whom 31 726 had obstructive CAD (52.0%), with significant range across hospitals from 37.3% to 69.2%. Fewer self-referral patients (49.8%) had obstructive CAD compared with nonself-referral patients (53.5%), with an odds ratio of 0.89 (95% confidence interval, 0.86-0.93; P<0.001). Angiograms performed by invasive physicians had a lower likelihood of obstructive CAD compared with those by interventional physicians (48.2% versus 56.9%; odds ratio, 0.85; 95% confidence interval, 0.81-0.90; P<0.001). Fewer angiograms at diagnostic only centers showed obstructive CAD (42.0%) compared with full service centers (55.1%; odds ratio, 0.62; 95% confidence interval, 0.39-0.98; P=0.04). Nonclinical factors accounted for 23.8% of the variation between hospitals. CONCLUSIONS: Physician and system factors are important predictors of obstructive CAD with coronary angiography.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".