Sensitivity of the Aortic Dissection Detection Risk Score, a Novel Guideline-Based Tool for Identification of Acute Aortic Dissection at Initial Presentation
Bibliographic record
Abstract
BACKGROUND: In 2010, the American Heart Association and American College of Cardiology released guidelines for the diagnosis and management of patients with thoracic aortic disease, which identified high-risk clinical features to assist in the early detection of acute aortic dissection. The sensitivity of these risk markers has not been validated. METHODS AND RESULTS: We examined patients enrolled in the International Registry of Acute Aortic Dissection from 1996 to 2009. The number of patients with confirmed acute aortic dissection who presented with 1 or more of 12 proposed clinical risk markers was determined. An aortic dissection detection (ADD) risk score of 0 to 3 was calculated on the basis of the number of risk categories (high-risk predisposing conditions, high-risk pain features, high-risk examination features) in which patients met criteria. The ADD risk score was tested for sensitivity. Of 2538 patients with acute aortic dissection, 2430 (95.7%) were identified by 1 or more of 12 proposed clinical risk markers. With the use of the ADD risk score, 108 patients (4.3%) were identified as low risk (ADD score 0), 927 patients (36.5%) were intermediate risk (ADD score 1), and 1503 patients (59.2%) were high risk (ADD score 2 or 3). Among 108 patients with no clinical risk markers present (ADD score 0), 72 had chest x-rays recorded, of which 35 (48.6%) demonstrated a widened mediastinum. CONCLUSIONS: The clinical risk markers proposed in the 2010 thoracic aortic disease guidelines and their application as part of the ADD risk score comprise a highly sensitive clinical tool for the detection of acute aortic dissection.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".