Abstract TP221: Prevelance of Triggers and Risk Factors in Cervical Artery Dissection
Bibliographic record
Abstract
Introduction: Cervical artery dissection (CAD) accounts for nearly 2% of all ischemic strokes but up to 25% of ischemic strokes in young adults. Dissection is likely precipitated by the interplay between risk factors (migraine, low body mass index), environmental triggers (cervical trauma or infection), and genetic connective tissue abnormalities (e.g. Ehlers-Danlos or Marfan’s disease). In this study, we delineate the prevalence of triggers and risk factors in a multicenter cohort of cervical artery dissection. Methods: This is a post-hoc analysis of the Antithrombotic for Stroke Prevention in Cervical Artery Dissection Study (STOP-CAD). We recorded information using the admission data on risk factors (migraine), triggers (upper respiratory infection, COVID-19, and minor cervical injury), and whether the patient had a known connective tissue disorder (CTD). We determined the prevalence of risk factors, triggers, and presence of CTD in patients with cervical artery dissection as well as the interplay between these factors in the pathogenesis of CAD. Results: We identified 4023 patients with CAD, the mean age was 47 years and 45% were women. A history of migraines was present in 16.6% (668) patients. At least one environmental trigger was present in 26.3% of patients (1061 patients), with minor cervical injury being the most common (22.2%, 892 patients), then upper respiratory infections (6.2%, 251 patients), and COVID-19 (1.2%, 49 patients). Among cervical injury, the most common was chiropractic manipulation (5.7%, 228 patients). Only 2% (83 patients) were known to have a CTD with Fibromuscular Dysplasia being the most common (0.6%, 23 patients). Among the entire STOP-CAD cohort, only 5 patients (0.1%) had evidence of at least one trigger, risk factor, or a known connection tissue disease. On the other hand, 61% of patients (2441 patients) had none of the three recorded. Conclusion: In patients with CAD, the absence of any risk factor, trigger, or known CTD is common and should not lead to dismissing a dissection diagnosis in patients with symptoms concerning for CAD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".