Abstract 13615: Diagnostic Accuracy of Cardiac MRI for Detecting Wall Motion Abnormalities and Coronary Artery Stenosis - A Systematic Review and Umbrella Meta-Analysis
Bibliographic record
Abstract
Introduction: Conventional coronary angiography (CCA) is the gold standard for the diagnosis of coronary artery disease (CAD). It is an invasive test and carries risks. Increased mortality is linked with reduced coronary flow, thus aggressively measuring coronary flow is important. Cardiac MRI (cMRI) is an emerging non-invasive test that can also help in assessing ventricular function, myocardial perfusion, and detecting anomalies. Aim: We aimed to analyze the diagnostic accuracy of cMRI compared to CCA through an umbrella meta-analysis. Methods: We searched PubMed articles comparing the diagnostic accuracy of cMRI with CCA in patients having CAD published from 2000 to 2022 according to PRISMA guidelines. We used the generic inverse variance method to pool sensitivity (SN), specificity (SP), negative likelihood ratio, positive likelihood ratio, and diagnostic odds ratio (DOR) with 95% confidence interval keeping alpha criteria of 0.05 as significant. RevMan 5.4 was used to calculate random effects models. Results: In this umbrella meta-analysis, 9 studies with 14615 patients met inclusion criteria. Overall pooled SN was 0.88 (95% CI: 0.87-0.90), SP was 0.85 (0.81-0.89), PLR was 6.07 (5.02-7.33), NLR was -1.99 (-2.10 to -1.87), and DOR 38.94 (31.96-47.46). (p<0.00001) Conclusion: The diagnostic accuracy of a non-invasive, cMRI is comparable, if not superior to an invasive test like CCA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".