Mortality and risk factors associated with misdiagnosis of acute aortic syndrome in Ontario, Canada: a population-based study
Bibliographic record
Abstract
Introduction Acute aortic syndrome (AAS) is a life-threatening aortic emergency. It describes three diagnoses: acute aortic dissection, acute intramural haematoma and penetrating atherosclerotic ulcer. Unfortunately, there are no accurate estimates of the miss rate for AAS, risk factors for missed diagnosis or its effect on outcomes. Methods A population-based retrospective cohort study of anonymously linked data for residents of Ontario, Canada, was carried out. Incident cases of AAS were identified between 2003 and 2018 using a validated algorithm based on ICD codes and death. Before multivariate modelling, all categorical variables were analysed for an association with missed AAS diagnosis using χ2tests. These preliminary analyses were unadjusted for clustering or any covariates. Finally, we performed multilevel logistic regression analysis using a generalised linear mixed model approach to model the probability of a missed case occurring. Results There were 1299 cases of AAS (age mean (SD) 68.03±14.70, woman 500 (38.5%), rural areas (n=111, 8.55%)) over the study period. Missed cases accounted for 163 (12.5%) of the cohort. Mortality (non-missed AAS 59.7% vs missed AAS 54.6%) and surgical intervention (non-missed AAS 31% vs missed AAS 30.7%) were similar in missed and non-missed cases. However, lower acuity (Canadian triage acuity scale >2 (OR 2.45 95% CI 1.71 to 3.52) (the scale is from 1 to 5, with 1 indicating high acuity) had a higher odds of being a missed case and non-ambulatory presentation (OR 0.47 95% CI 0.33 to 0.67) and presenting to a teaching (OR 0.60 95% CI 0.40 to 0.90)) or cardiac centre (OR 0.41 95% CI 0.27 to 0.62) were associated with a lower odds of being a missed case. Conclusions The high rate of misdiagnosis has remained stable for over a decade. Non-teaching and non-cardiac hospitals had a higher incidence of missed cases. Mortality and rates of surgery were not associated with a missed diagnosis of AAS. Educational interventions should be prioritised in non-teaching hospitals and non-cardiac centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".