Preprocedural computed tomography angiography in differentiating chronic total from subtotal coronary occlusions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it