Preprocedural computed tomography angiography in differentiating chronic total from subtotal coronary occlusions
Bibliographic record
Abstract
INTRODUCTION: Differentiation of chronic total occlusion (CTO) from subtotal coronary occlusions (STOs) is often difficult to make from coronary angiography. These differences are very important, as the technical expertise and tools required are significantly different for revascularization of these lesions. We sought to determine if preprocedural computed tomography angiography (CTA) can help better diagnose and differentiate CTO from STO. METHODS: We searched three databases (Ovid MEDLINE, EMBASE, EBM reviews) from 1 January 1946 to 1 March 2019. Studies reporting on the use of computed tomography (CT) to aid in CTO revascularization were included. Case reports and case series were excluded. RESULTS: We identified 577 articles, and using the Preferred Reporting Items for Systematic Reviews and Meta-analyses method, 4 articles met prespecified inclusion criteria. A total of 669 patients were included. The statistically significant CT-derived parameters determined to help differentiate CTO from STO were found to include longer lesion length (four out of four studies), larger contrast density difference (one out of four studies), presence of collaterals (two out of four studies) and the presence of the reverse attenuation gradient sign (two out of four studies). CONCLUSION: This systematic review shows the utility of preprocedural CTA to help differentiate CTO from STO using a number of CT-derived parameters as above. Further, this study highlights the need for further research to develop specific validated parameters for differentiation of CTO and STO.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".