Anomalous Right Coronary Artery: A Multimodality Hunt for the Origin
Bibliographic record
Abstract
Anomalous origin of the right coronary artery from the pulmonary artery (ARCAPA) is a rare congenital anomaly. Although there have been several cases of ARCAPA reported in the literature, we present a case which highlights the challenges of diagnosing this rare condition and the incremental value of using multiple imaging modalities. A healthy 48 year old female presented with angina and exertional shortness of breath. She had a normal cardiovascular examination, negative cardiac enzymes and an unremarkable chest X-ray. She did, however, have T-wave inversions in leads V1-V3. Transthoracic echocardiography (TTE), as the first imaging investigation, led to an initial provisional diagnosis of a coronary-cameral fistula. It showed unusual colour Doppler signals in the right ventricle and a prominent pattern of diastolic flow within the right ventricular myocardium, especially along the interventricular septum. A subsequent multimodality approach, correlating images from angiography, CT and MRI was instrumental in confirming the diagnosis of ARCAPA and planning for surgical correction. Cardiac CT and MRI are non-invasive, three-dimensional imaging modalities with high diagnostic accuracy for coronary artery anatomic anomalies. If echocardiography and conventional angiography have been inconclusive, cardiac CT and MRI are especially important diagnostic tools.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".