Chest pain investigation in patients at low or intermediate risk: What is the best first-line test to rule out coronary artery disease?
Bibliographic record
Abstract
OBJECTIVE: To compare different methods for investigation of stable, low-risk chest pain and identify the best first-line test for patients at low or intermediate risk of coronary artery disease (CAD). SOURCES OF INFORMATION: The MEDLINE database was searched for articles on chest pain investigation from 1978 to 2019 using the following key terms: chest pain, exercise electrocardiography, nuclear perfusion imaging, stress echocardiography, cardiovascular magnetic resonance imaging, coronary computed tomography angiography, and catheter angiography. Results were limited to English-language, peer-reviewed journal publications. MAIN MESSAGE: Chest pain is a common chief concern among patients presenting to primary care physicians. Several investigative options exist, and each method has inherent strengths and limitations resulting in variable performance depending on the pretest likelihood of CAD. Controversy exists regarding which is the best first-line test among low- or intermediate-risk patients. Coronary computed tomography angiography is emerging as the best first-line test for chest pain investigation in patients who are at low or intermediate risk of CAD. Invasive catheter angiography remains the reference standard, although it is usually reserved for high-risk patients or for confirmation of positive noninvasive test results. CONCLUSION: Several investigative options exist for the evaluation of stable, low-risk chest pain. A review of the literature reveals an emerging role for CCTA as a first-line test, particularly in low- or intermediate-risk populations without known 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".