Abstract 155: Misdiagnosis in Cervical Artery Dissection: Analysis of STOP-CAD Study
Bibliographic record
Abstract
Background: Cervical artery dissection (CeAD) accounts for a quarter of strokes in young adults. Clinical presentation is often non-specific, leading to a significant proportion of cases being misdiagnosed. This study aims to identify factors contributing to potential misdiagnosis and to assess the impact of misdiagnosis on patient outcomes. Methods: This is a secondary analysis of the Antithrombotics for Stroke Prevention in Cervical Artery Dissection (STOP-CAD), a multicenter cross-sectional international retrospective study of adult patients presenting to an acute care hospital and diagnosed with CeAD. Misdiagnosis of CeAD was determined by identifying any medical encounters for symptoms attributable to the CeAD within the 30 days preceding the diagnosis. Strokes occurring during the interval between the initial misdiagnosis encounter and the final diagnosis date were recorded. We used multivariable regression to identify factors associated with misdiagnosis. The primary outcome was ischemic stroke after CeAD diagnosis. Secondary outcomes included death and excellent functional outcome at 90 days, defined as a modified Rankin Scale (mRS) score <2. Multivariable logistic regression analysis was used to assess the association between misdiagnosis and above outcomes. Results: Of the 4023 patients included in the STOP-CAD study, 4012 were included in this analysis; mean age was 47.5 (+/-13.3) years and 44.6% (1788) were woman. Among these, 663 patients (16.5%) had medical encounters within the 30 days preceding the diagnosis (misdiagnosis group) and 224 (33.8%) had an ischemic stroke after the initial medical encounter but before the CeAD diagnosis. Misdiagnosed patients were younger (OR 0.89, 95% CI 0.83-0.95, p<0.001), more likely to have a history of migraine (OR 1.35, 95% CI 1.09-1.67, p=0.007), present with headache (OR 1.43, 95% CI 1.19-1.71, p<0.001) and less likely to present with signs and symptoms concerning for cerebral ischemia (OR 0.68, 95% CI 0.56-0.83, p<0.001). There was no significant difference in outcomes between the two groups in adjusted analyses: ischemic stroke (aOR 0.96, 95%CI 0.63-1.48, p=0.87), mRS<2 (aOR 1.04, 95% CI 0.76-1.44, p=0.80) and death (aOR 0.58, 95% CI 0.19-1.8, p=0.34). Conclusion: One in six patients who were ultimately diagnosed with CeAD experienced a possible misdiagnosis. Factors associated with a higher likelihood of misdiagnosis included younger age, a history of migraine, and a non-ischemic clinical presentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".