What Is the Specificity of the Aortic Dissection Detection Risk Score in a Low‐prevalence Population?
Bibliographic record
Abstract
BACKGROUND: Acute aortic syndrome (AAS) is a time-sensitive and difficult-to-diagnose aortic emergency. The American Heart Association (AHA) proposed the acute aortic dissection detection risk score (ADD-RS) as a means to reduce miss rate and improve time to diagnosis. Previous validation studies were performed in a high prevalence population of patients. We do not know how the rule will perform in a lower-prevalence population. This is important because application of a rule with low specificity would increase imaging rates and complications. Our goal was to assess if the diagnostic accuracy of the score would be maintained in a low-prevalence population that we are attempting to risk stratify in the emergency department (ED). METHODS: Retrospective cohort of patients age 18 years old and older who presented to two tertiary care EDs from January 1, 2015, to December 31, 2015, and underwent a computed tomographic angiography to rule out AAS. Two trained reviewers extracted data using a standardized data collection form. AAS was defined according to accepted radiologic standards. The components of the AHA risk score were defined a priori. Agreement was measured using kappa statistic. Sensitivity, specificity, and positive and negative likelihood ratios with 95% confidence intervals (CIs) were calculated. Analysis was performed using SAS 9.4 University Edition. RESULTS: A total 370 patients underwent computed tomography for suspected AAS. Chief presenting symptoms were chest pain (207, 58%), back pain (26, 7%), abdominal pain (32, 8.6%), syncope (7, 2.6%), and symptoms of stroke (6, 1.6%). AAS was finally diagnosed in 12 (3.2%) patients: five (1.4%) type A aortic dissection, four (1%) type B aortic dissection, two (0.5%) an aortic intramural hematoma, no penetrating aortic ulcer, and one a ruptured abdominal aortic aneurysm. The presence of one or more ADD risk markers (ADD-RS ≥ 1) was associated with a sensitivity of 100% (95% CI = 73.5%-100%) and a specificity of 12.3% (95% CI = 9.1%-16.2%) for the diagnosis of AAS. The negative likelihood ratio was 0 and the positive likelihood ratio was 1.14 (95% CI = 1.1-1.2). CONCLUSIONS: Our study confirms that in North America the prevalence of AAS in those undergoing advanced imaging is low. The ADD-RS in this population has a low specificity. A lack of defined inclusion criteria and a low specificity limits the application of this rule in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".