Accuracy of presenting symptoms, physical examination, and imaging for diagnosis of ruptured abdominal aortic aneurysm: Systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Ruptured abdominal aortic aneurysm (rAAA) is a life-threatening condition, and rapid diagnosis is necessary to facilitate early surgical intervention. We sought to evaluate the accuracy of presenting symptoms, physical examination signs, computed tomography with angiography (CTA), and point-of-care ultrasound (PoCUS) for diagnosis of rAAA. METHODS: We searched six databases from inception through April 2021. We included studies investigating the accuracy of any of the above tests for diagnosis of rAAA. The primary reference standard used in all studies was intraoperative diagnosis or death from rAAA. Because PoCUS cannot detect rupture, we secondarily assessed its accuracy for the diagnosis of AAA, using the reference standard of intraoperative or CTA diagnosis. We used GRADE to assess certainty in estimates. RESULTS: We included 20 studies (2,077 patients), with 11 of these evaluating signs and symptoms, seven evaluating CTA, and five evaluating PoCUS. Pooled sensitivities of abdominal pain, back pain, and syncope for rAAA were 61.7%, 53.6%, and 27.8%, respectively (low certainty). Pooled sensitivity of hypotension and pulsatile abdominal mass were 30.9% and 47.1%, respectively (low certainty). CTA had a sensitivity of 91.4% and specificity of 93.6% for diagnosis of rAAA (moderate certainty). In our secondary analysis, PoCUS had a sensitivity of 97.8% and specificity of 97.0% for diagnosing AAA in patients suspected of having rAAA (moderate certainty). CONCLUSIONS: Classic clinical symptoms associated with rAAA have poor sensitivity, and their absence does not rule out the condition. CTA has reasonable accuracy, but misses some cases of rAAA. PoCUS is a valuable tool that can help guide the need for urgent transfer to a vascular center in patients suspected of having rAAA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".