Prevalence of dual left anterior descending artery variations in CT angiography
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the frequency and features of dual left anterior descending artery (LAD) variants using computed tomography (CT) angiography. METHODS: A total of 1337 consecutive coronary CT angiography examinations performed between April 2010 and December 2013 were retrospectively evaluated for the presence of dual LAD. CT examinations were performed with either 64- or 320-row multidetector CT scanners. All CT angiography images were evaluated for the presence and morphologic features of dual LAD subtypes. RESULTS: Fifty-six dual LAD variations (4%) were identified in this study population. Type 1 was the most common type of dual LAD (n=48), while Type 3 (n=3) and Type 4 (n=2) were infrequent and Type 2 was not detected. Additionally, we detected previously unclassified dual LAD variations in three cases. CONCLUSION: Dual LAD may be a relatively more common variant than described in the medical literature, which is mostly based on catheter angiography studies. Coronary CT angiography seems markedly efficacious for detecting and documenting the anatomical details of dual LAD subtypes, as well as showing other associated cardiocoronary anomalies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".