Algorithms and clinical decision-making tools for ruling out acute aortic syndrome in the emergency department: a narrative review
Bibliographic record
Abstract
Acute aortic syndrome (AAS) remains one of the most challenging diagnoses for Emergency Physicians. Given the time-sensitive nature of this highly fatal disease state, rapid and accurate testing modalities and diagnostic algorithms are sorely needed to reduce the high rates of missed diagnoses in the emergency department (ED). Several clinical tools and scoring systems have been proposed over the past decade in an effort to assist physicians in achieving this end and thus improve overall patient morbidity and mortality with more prompt identification and subsequent intervention. These are often based on prior expert guidelines, high-risk clinical features well-established in AAS, biomarkers, imaging studies, and other metrics regularly obtained in the ED. Unfortunately, all algorithms and clinical decision-making tools currently available have yet to be externally, prospectively validated to provide a reliable, definitive means of either diagnosing or ruling-out AAS in the ED. However, research is ongoing, the literature remains rather robust, and continued efforts are underway to develop and validate an optimal tool for widespread, standardized use for this “never-miss” diagnosis in emergency medicine.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".