High Risk Clinical Features for Acute Aortic Dissection: A Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Acute aortic dissection (AAD) is a rare condition with a high mortality that is often missed. The objective of our study was to assess the diagnostic accuracy of clinical and laboratory findings for AAD, in confirmed cases of AAD and in a low-risk control group. METHODS: This was a historical matched case-control study: participants were adults > 18 years old presenting to two tertiary care emergency departments (EDs) or one regional cardiac referral center. Cases were patients with new ED or in-hospital diagnosis of nontraumatic AAD confirmed by computed tomography or echocardiography. Controls were patients with a triage diagnosis of truncal pain (<14 days) and an absence of a clear diagnosis on basic investigation. Cases and controls were matched in a 1:4 ratio by sex and age. A sample size of 165 cases and 660 controls was calculated based on 80% power and confidence interval of 95% to detect an odds ratio of greater than 2. RESULTS: Data were collected from 2002 to 2014 yielding 194 cases of AAD and 776 controls (mean ± SD age = 65 ± 14.1 years; 66.7% male). Absence of abrupt-onset pain (sensitivity = 95.9%, negative likelihood ratio = 0.07 [0.03-0.14]) can help rule out AAD. Presence of tearing/ripping pain (specificity = 99.7%, positive likelihood ratio [LR+] = 42.1 [9.9-177.5]), aortic aneurysm (specificity = 97.8%, LR+ = 6.35 [3.54-11.42]), hypotension (specificity = 98.7%, LR+ = 17.2 [8.8-33.6]), pulse deficit (specificity = 99.3, LR+ = 31.1 [11.2-86.6]), neurologic deficits (specificity = 96.9%, LR+ = 5.26 [2.9-9.3]), and a new murmur (specificity = 97.8%, LR+ = 9.4 [5.5-16.2]) can help rule in the diagnosis of AAD. CONCLUSIONS: Patients with one or more high-risk feature should be considered high risk, whereas patients with no high-risk and multiple low-risk features are at low risk for AAD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".