Incidence Trends and Risk of Recurrent Stroke of Cervical Artery Dissections in the United States Between 2005 and 2019
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cervical artery dissection (CeAD) is a common cause of acute ischemic stroke (AIS), especially in patients younger than 55 years, but data regarding trends and subsequent AIS risk after CeAD remain scarce. We aimed to determine national trends in CeAD admissions and examine post-CeAD risk of ischemic stroke. METHODS: codes. Survey-weighted annual CeAD cases were combined with US census data to estimate annual incidence. National estimates were verified with state-level data, which allows for the removal of duplicate admissions for a single patient through a unique patient identifier. Joinpoint regression was used to quantify the average annual percent change (AAPC) of CeAD incidence. AIS readmission risk after CeAD without concurrent AIS was assessed with death as a competing risk using Fine and Gray competing risk methodology. RESULTS: = 0.006). Interaction and subgroup analyses were performed and demonstrated similar results. DISCUSSION: There was an almost 5-fold increase in CeAD hospitalizations and an upward incidence trend from 2005 to 2019, particularly in racial minorities, which may be attributed to increased imaging and awareness of CeAD. Our study also revealed a small but significant risk of AIS in patients with vertebral artery dissection without concurrent ischemic stroke. These findings underscore the importance of studying acute treatment and secondary prevention strategies in patients with CeAD.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".