Triple Rule-out Computed Tomographic Angiography for Chest Pain: A Diagnostic Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: The objective was to compare the image quality, diagnostic accuracy, radiation exposure, and contrast volume of "triple rule-out" (TRO) computed tomography (CT) to other diagnostic modalities commonly used to evaluate patients with nontraumatic chest pain (dedicated coronary, pulmonary embolism [PE], and aortic dissection CT; invasive coronary angiography; and nuclear stress testing). METHODS: Four electronic databases were searched, along with reference lists and contacted content experts, for relevant studies from inception until October 2012. Eligible studies enrolled patients with nontraumatic chest pain, shortness of breath, suspected acute coronary syndrome (ACS), PE, or aortic dissection; used at least 64-slice CT technology; and compared TRO CT to another diagnostic modality. RESULTS: Eleven studies enrolling 3,539 patients (791 TRO and 2,748 non-TRO) were included (one randomized controlled trial and 10 observational). There was no significant difference in image quality between TRO and dedicated CT scans. TRO CT had the following pooled diagnostic accuracy estimates for coronary artery disease: sensitivity of 94.3% (95% confidence interval [CI] = 89.1% to 97.5%), specificity of 97.4% (95% CI = 96.1% to 98.4%), positive likelihood ratio (LR+) of 17.71 (95% CI = 3.92 to 79.96), and negative likelihood ratio (LR-) of 0.08 (95% CI = 0.02 to 0.27). There were insufficient numbers of patients with PE or aortic dissection to generate diagnostic accuracy estimates for these conditions. Use of TRO CT involved greater radiation exposure (mean difference [MD] = 4.84 mSv, 95% CI = 1.65 to 8.04 mSv) and contrast exposure (MD = 38.0 mL, 95% CI = 28.1 to 48.0 mL) compared to non-TRO CT patients. CONCLUSIONS: Triple rule-out CT is highly accurate for detecting coronary artery disease. Given the low (<1%) prevalence of PE and aortic dissection in the included studies, and the increased radiation and contrast exposure, there are insufficient data to recommend use of TRO CT in the diagnosis of these conditions.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".