Functional testing after coronary artery bypass graft surgery: a meta-analysis.
Bibliographic record
Abstract
BACKGROUND: A number of studies have examined the diagnostic abilities of various functional tests to assess graft stenosis or the progression of coronary artery disease after coronary artery bypass graft (CABG) surgery. However, a meta-analysis of these studies has not been performed. OBJECTIVES: To pool the results of studies examining the diagnostic abilities of exercise treadmill testing (ETT), stress myocardial perfusion imaging and stress echocardiography to predict graft stenosis or progression of disease in the native circulation post-CABG. METHODS: A MEDLINE search was conducted to identify studies examining post-CABG functional testing for the diagnosis of graft stenosis or progression of native disease. Sensitivities and specificities of these studies were pooled, and predictive values and likelihood ratios were calculated. RESULTS: A pooled analysis demonstrates that for the identification of graft stenosis or progression of native disease, ETT alone has a sensitivity of 45% (95% CI 36% to 54%) and a specificity of 82% (95% CI 68% to 95%). The use of stress myocardial perfusion imaging increased the sensitivity to 68% (95% CI 51% to 86%) and specificity to 84% (95% CI 78% to 91%). The use of stress echocardiography also resulted in an increased sensitivity of 86% (95% CI 78% to 94%) and specificity of 90% (95% CI 84% to 95%). CONCLUSION: If post-CABG functional testing is performed, stress ventricular imaging is superior to ETT alone for the diagnosis of graft stenosis or progression of disease in the native vessels.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.045 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".