Diagnostic and prognostic significance of transient ischemic dilation (TID) in myocardial perfusion imaging: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Transient ischemic dilatation (TID) of the left ventricle is a potential marker of high risk obstructive coronary artery disease on stress myocardial perfusion imaging (MPI). There is, however, interstudy variation in the diagnostic performance of TID for identification of severe and extensive coronary disease anatomy, and varied prognostic implications in the published literature. METHODS: We searched MEDLINE, EMBASE, and COCHRANE databases for studies where TID was compared with invasive or CT coronary angiography for evaluation of coronary artery stenosis. Two reviewers independently evaluated and abstracted data from each study. A bivariate random effects model was used to derive pooled sensitivities and specificities, in order to account for correlation between TID in MPI and anatomic disease severity. RESULTS: A total of 525 articles were reviewed, of which 51 met inclusion criteria. Thirty-one studies contributed to the analysis, representing a total of 2037 patients in the diagnostic meta-analysis and 9003 patients in the review of prognosis. The ratio above which TID was deemed present ranged from 1.13 to 1.38. Pooled sensitivity was 44% (95% CI 30%-60%) and specificity was 88% (95% CI 83%-92%) for the detection of extensive or severe anatomic coronary artery disease. Analysis of outcome data demonstrated increased cardiac event rates in patients with TID and an abnormal MPI. In otherwise normal perfusion, TID is an indicator of poor prognosis in patients with diabetes and/or a history of coronary disease. CONCLUSIONS: Among patients undergoing MPI, the presence of TID is specific for the detection of extensive or severe coronary artery disease.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| 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".