The prospective randomized trial of the optimal evaluation of cardiac symptoms and revascularization: Rationale and design of the PRECISE trial
Bibliographic record
Abstract
Clinicians vary widely in their preferred diagnostic approach to patients with non-acute chest pain. Such variation exposes patients to potentially avoidable risks, as well as inefficient care with increased costs and unresolved patient concerns. The Prospective Randomized Trial of the Optimal Evaluation of Cardiac Symptoms and Revascularization (PRECISE) trial (NCT03702244) compares an investigational “precision” diagnostic strategy to a usual care diagnostic strategy in participants with stable chest pain and suspected coronary artery disease (CAD). PRECISE randomized 2103 participants with stable chest pain and a clinical recommendation for testing for suspected CAD at 68 outpatient international sites. The investigational precision evaluation strategy started with a pre-test risk assessment using the PROMISE Minimal Risk Tool. Those at lowest risk were assigned to deferred testing (no immediate testing), and the remainder received coronary computed tomographic angiography (cCTA) with selective fractional flow reserve (FFRCT) for any stenosis meeting a threshold of ≥30% and <90%. For participants randomized to usual care, the clinical care team selected the initial noninvasive or invasive test (diagnostic angiography) according to customary practice. The use of cCTA as the initial diagnostic strategy was proscribed by protocol for the usual care strategy. The primary endpoint is time to a composite of major adverse cardiac events (MACE: all-cause death or non-fatal myocardial infarction) or invasive cardiac catheterization without obstructive CAD at 1 year. Secondary endpoints include health care costs and quality of life. PRECISE will determine whether a precision approach comprising a strategically deployed combination of risk-based deferred testing and cCTA with selective FFRCT improves the clinical outcomes and efficiency of the diagnostic evaluation of stable chest pain over usual care.
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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 | 0.081 | 0.077 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".