The diagnostic accuracy and outcomes after coronary computed tomography angiography vs. conventional functional testing in patients with stable angina pectoris: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: To systematically review and perform a meta-analysis of the diagnostic accuracy and post-test outcomes of conventional exercise electrocardiography (XECG) and single-photon emission computed tomography (SPECT) compared with coronary computed tomography angiography (coronary CTA) in patients suspected of stable coronary artery disease (CAD). METHODS AND RESULTS: We systematically searched for studies published from January 2002 to February 2013 examining the diagnostic accuracy (defined as at least ≥50% luminal obstruction on invasive coronary angiography) and outcomes of coronary CTA (≥16 slice) in comparison with XECG and SPECT. The search revealed 11 eligible studies (N = 1575) comparing the diagnostic accuracy and 7 studies (N = 216.603) the outcomes of coronary CTA vs. XECG or/and SPECT. The per-patient sensitivity [95% confidence interval (95% CI)] to identify significant CAD was 98% (93-99%) for coronary CTA vs. 67% (54-78%) (P < 0.001) for XECG and 99% (96-100%) vs. 73% (59-83%) (P = 0.001) for SPECT. The specificity (95% CI) of coronary CTA was 82% (63-93%) vs. 46% (30-64%) (P < 0.001) for XECG and 71% (60-80%) vs. 48% (31-64%) (P = 0.14) for SPECT. The odds ratio (OR) of downstream test utilization (DTU) for coronary CTA vs. XECG/SPECT was 1.38 (1.33-1.43, P < 0.001), for revascularization 2.63 (2.50-2.77, P < 0.001), for non-fatal myocardial infarction 0.53 (0.39-0.72, P < 0.001), and for all-cause mortality 1.01 (0.87-1.18, P = 0.87). CONCLUSION: The up-front diagnostic performance of coronary CTA is higher than of XECG and SPECT. When compared with XECG/SPECT testing, coronary CTA testing is associated with increased DTU and coronary revascularization.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".