Computed Tomography and Magnetic Resonance Imaging of the Coronary Sinus: Anatomic Variants and Congenital Anomalies
Bibliographic record
Abstract
The coronary sinus (CS) is an important vascular structure that allows for access into the coronary veins in multiple interventional cardiology procedures, including catheter ablation of arrhythmias, pacemaker implantation and retrograde cardioplegia. The success of these procedures is facilitated by the knowledge of the CS anatomy, in particular the recognition of its variants and anomalies. This pictorial essay reviews the spectrum of CS anomalies, with particular attention to the distinction between clinically benign variants and life-threatening defects. Emphasis will be placed on the important role of cardiac CT and cardiovascular magnetic resonance in providing detailed anatomic and functional information of the CS and its relationship to surrounding cardiac structures. Teaching Points • Cardiac CT and cardiovascular magnetic resonance offer 3D high-resolution mapping of the coronary sinus in pre-surgical planning.• Congenital coronary sinus enlargement occurs in the presence or absence of a left-to-right shunt.• Lack of recognition of coronary sinus anomalies can lead to adverse outcomes in cardiac procedures.• In coronary sinus ostial atresia, coronary venous drainage to the atria occurs via Thebesian or septal veins.• Coronary sinus diverticulum is a congenital outpouching of the coronary sinus and may predispose to cardiac arrhythmias.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".