Intraluminal Filling Defects on Coronary Angiography: More than Meets the Eye
Bibliographic record
Abstract
Intraluminal filling defects are occasionally encountered on coronary angiography and often related with coronary thrombi. However, other conditions affecting the coronary arteries may present with similar angiographic findings causing diagnostic uncertainty. Accurate characterization of the angiographic filling defect is critical, particularly in patients planned for a percutaneous coronary intervention (PCI), as diagnosis of a coronary thrombus not only increases the risk of post procedural adverse events but also requires a specific therapeutic approach. In this paper, we report three patients in whom coronary angiography revealed intraluminal filling defects mimicking coronary thrombi. When further investigated with intravascular ultrasound (IVUS) as a part of the planned PCI, the thrombus was excluded and alternate etiology of the filling defect was confirmed in all patients. The angiographic "pseudothrombi" were produced by coronary dissection in one and by heavy calcification within the atherosclerotic plaque in two patients. The use of IVUS allowed accurate characterization of the angiographic filling defect and provided important information to guide management and optimize therapeutic approach.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".