Prevalence of Acute Coronary Syndrome in Patients Suspected for Pulmonary Embolism or Acute Aortic Syndrome: Rationale for the Triple Rule-Out Concept
Bibliographic record
Abstract
BACKGROUND: The aims of the study were to evaluate the prevalence of acute coronary syndrome (ACS) among patients presenting with atypical chest pain who are evaluated for acute aortic syndrome (AAS) or pulmonary embolism (PE) with computed tomoangiography (CTA) and discuss the rationale for the use of triple rule-out (TRO) protocol for triaging these patients. METHODS: This study is a retrospective analysis of patients presenting with atypical chest pain and evaluated with thoracic (CTA), for suspicion of AAS/PE. Two physicians reviewed patient files for demographic characteristics, initial CT and final clinical diagnosis. Patients were classified according to CTA finding into AAS, PE and other diagnoses and according to final clinical diagnosis into AAS, PE, ACS and other diagnoses. RESULTS: Four hundred and sixty-seven patients were evaluated: 396 (84.8%) patients for clinical suspicion of PE and 71 (15.2%) patients for suspicion of AAS. The prevalence of ACS and AAS was low among the PE patients: 5.5% and 0.5% respectively (P = 0.0001), while the prevalence of ACS and PE was 18.3% and 5.6% among AAS patients (P = 0.14 and P = 0.34 respectively). CONCLUSION: The prevalence of ACS and AAS among patients suspected clinically of having PE is limited while the prevalence of ACS and PE among patients suspected clinically of having AAS is significant. Accordingly patients suspected for PE could be evaluated with dedicated PE CTA while those suspected for AAS should still be triaged using TRO protocol.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".