2613 Diagnostic accuracy of the aortic dissection detection risk score alone or with D-dimer for acute aortic syndromes: systematic review and meta-analysis
Bibliographic record
Abstract
Aims and Objectives Acute aortic syndrome (AAS) is an uncommon but life-threatening diagnosis. Symptoms suggesting AAS are very common, so clinical scores and blood tests may be needed to select patients for definitive imaging. We aimed to evaluate the diagnostic accuracy of the aortic dissection detection risk score (ADD-RS) used alone or in combination with D-dimer for detecting AAS in patients presenting with symptoms suggestive of AAS. Method and Design We searched MEDLINE, EMBASE, and the Cochrane Library from inception to February 2024, along with the reference lists of included studies and other systematic reviews. All diagnostic accuracy studies that assessed the use of ADD-RS alone or with D-Dimer for diagnosing AAS compared with a reference standard test were included. Two reviewers independently selected and extracted data. Risk of bias was appraised using QUADAS-2 tool. Data were synthesised using hierarchical meta-analysis models. Results and Conclusion We selected 13 studies from the 2017 citations identified, including six studies evaluating combinations of ADD-RS alongside D-dimer>500ng/L. The methodological quality of the included studies was variable, with most studies having low or unclear risk of bias and applicability concerns in at least one item of the QUADAS-2 tool. The table shows the summary sensitivities and specificities (with 95% credible and predictive intervals) of ADD-RS at thresholds of greater than zero and greater than one, and combinations of the ADD-RS and D-dimer. The ADD-RS can be used alone or alongside D-dimer to identify AAS with a range of trade-offs between sensitivity (93.1% to 99.8%) and specificity (21.8% to 67.1%). A combination based on the Canadian Clinical Practice Guideline may represent the best trade-off, with sensitivity of 93.1% and specificity of 67.1% for AAS.
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 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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".