Recurrence of cervical artery dissection: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cervical artery dissection (CAD) involving the carotid or vertebral arteries is an important cause of stroke in younger patients. The purpose of this systematic review is to assess the risk of recurrent CAD. METHODS: A systematic review and meta-analysis was conducted on studies in which patients experienced radiographically confirmed dissections involving an extracranial segment of the carotid or vertebral artery and in whom CAD recurrence rates were reported. RESULTS: Data were extracted from 29 eligible studies (n = 5898 patients). Analysis of outcomes was performed by pooling incidence rates with random effects models weighting by inverse of variance. The incidence of recurrent CAD was 4% overall (95% confidence interval (CI) = 3-7%), 2% at 1 month (95% CI = 1-5%), and 7% at 1 year in studies with sufficient follow-up (95% CI = 4-13%). The incidence of recurrence associated with ischemic events was 2% (95% CI = 1-3%). CONCLUSIONS: We found low rates of recurrent CAD and even lower rates of recurrence associated with ischemia. Further patient-level data and clinical subgroup analyses would improve the ability to provide patient-level risk stratification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".