Aortic dissection in the ED: a medico-legal perspective on diagnostic delays and failures
Bibliographic record
Abstract
PURPOSE: Aortic dissection is a life-threatening emergency and delayed or missed diagnoses can result in significant morbidity and mortality for patients. METHODS: We conducted a quantitative and qualitative analysis of medico-legal cases from a national repository involving thoracic aortic dissection seen in the emergency department during a 10-year period. We thematically analyzed peer expert criticism of the care in these cases, including provider, team, and systems factors related to diagnostic issues. We also applied two clinical decision tools to explore whether use of these tools could have affected the diagnostic process. RESULTS: Of 3,531 medico-legal cases in the emergency department during our study period, just 43 were related to aortic dissection. Patients were primarily male (68.2%), presented for care at large urban centers (65.1%), and were triaged as urgent or emergent (72.1%). Thirty-six patients died of their aortic dissection. A thematic analysis identified atypical presentations, diagnostic anchoring, misinterpretation or misuse of tests, communication breakdowns, and resource limitations as common in these cases. CONCLUSIONS: Missed diagnoses of aortic dissection in the ED often result from a combination of cognitive, communication, and system-level factors. Understanding these contributors can inform evidence-based, systems-level supports and strategies to enhance diagnostic accuracy and reduce patient harm.
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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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".