The CatLet score: a new coronary angiographic scoring tool accommodating the variable coronary anatomy for the first time
Bibliographic record
Abstract
BACKGROUND: The SYNTAX score for decision makings or outcome predictions in coronary artery disease does not account for the variations in the coronary anatomy, which is a clear fallacy for patients with less typical anatomy than suggested by the SYNTAX score. The current study aimed to derive a new coronary angiographic scoring system accommodating the variability in the coronary anatomy. METHODS: The 17-myocardial segment model and laws of competitive blood supply and flow conservation were utilized to derive this new scoring system. RESULTS: We obtained 6 types of RCA dominance, 3 types of diagonal size and 3 types of left anterior descending artery (LAD) length, which together resulted in a total of 54 patterns of coronary artery circulation to account for the variability in the coronary anatomy among individuals. A Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system has been designed based on the above-mentioned reclassification scheme (htpp://www.catletscore.com, IE browser is required to run this calculator). CONCLUSIONS: This new CatLet angiographic scoring system accommodated the variability in the coronary anatomy and standardized the collection of the coronary angiographic data, which could facilitate the comparison and exchange of these data between different catheter labs. Its utility for predicting the clinical outcomes and standardizing the angiographic data collection will be investigated in a series of clinical trials enrolling "all-comers" with coronary artery disease (CAD).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".