Clinical Examination for Acute Aortic Dissection: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Abstract Objectives Acute aortic dissection is a life‐threatening condition due to a tear in the aortic wall. It is difficult to diagnose and if missed carries a significant mortality. Methods We conducted a librarian‐assisted systematic review of PubMed, MEDLINE, Embase, and the Cochrane database from 1968 to July 2016. Titles and abstracts were reviewed and data were extracted by two independent reviewers (agreement measured by kappa). Studies were combined if low clinical and statistical heterogeneity (I 2 < 30%). Study quality was assessed using the QUADAS‐2 tool. Bivariate random effects meta analyses using Revman 5 and SAS 9.3 were performed. Results We identified 792 records: 60 were selected for full text review, nine studies with 2,400 participants were included (QUADAS‐2 low risk of bias, κ = 0.89 [for full‐text review]). Prevalence of aortic dissection ranged from 21.9% to 76.1% (mean ± SD = 39.1% ± 17.1%). The clinical findings increasing probability of aortic dissection were 1) neurologic deficit ( n = 3, specificity = 95%, positive likelihood ratio [LR+] = 4.4, 95% confidence interval [CI] = 3.3–5.7, I 2 = 0%) and 2) hypotension ( n = 4, specificity = 95%, LR+ = 2.9 95% CI = 1.8–4.6, I 2 = 42%), and decreasing probability were the absence of a widened mediastinum ( n = 4, sensitivity = 76%‐95%, negative likelihood ratio [LR–] = 0.14–0.60, I 2 = 93%) and an American Heart Association aortic dissection detection (AHA ADD) risk score < 1 ( n = 1, sensitivity = 91%, LR– = 0.22, 95% CI = 0.15–0.33). Conclusions Suspicion for acute aortic dissection should be raised with hypotension, pulse, or neurologic deficit. Conversely, a low AHA ADD score decreases suspicion. Clinical gestalt informed by high‐ and low‐risk features together with an absence of an alternative diagnosis should drive investigation for 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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".