Cervical arteries tortuosity and its association with dissection: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study investigated the link between arterial tortuosity and cervical artery dissection, focusing on carotid and vertebral tortuosity indices, as well as carotid tortuosity classifications (kinking, looping, and coiling). METHODS: We searched PubMed, SCOPUS, Web of Science, and Google Scholar from database inception to January 2024. The inclusion criteria encompassed human studies on tortuosity and cervical, carotid, or vertebral artery dissection. Exclusion criteria included case reports, non-English studies, and studies solely on connective tissue disorders and diseases. Quality and risk of bias were assessed using the Newcastle-Ottawa Scale. Random-effects model was employed for mean differences and odds ratios. When meta-analysis was not feasible, we summarized and integrated the results narratively. RESULTS: Seven studies, involving 507 dissected patients and 582 non-dissected patients, were included. In a meta-analysis of 3 studies, vertebral tortuosity favored the dissection cases [MD = 3.58, 95% CI: 2.21-4.95]. The mean carotid tortuosity difference was not statistically significant in a meta-analysis of 2 studies [MD = 2.27, 95% CI: -0.16-4.70]. In the classification analysis, 2 studies indicated no conclusive association between kinking, coiling, and cervical arteries dissection. Regarding carotid classification and internal carotid artery dissection, meta-analyses only showed a significant association with kinking, but the result was inconclusive. CONCLUSION: Tortuosity index screenings may help prevent cervical artery dissection among at-risk individuals. However, the association with specific tortuosity classifications remains inconclusive, and further research is needed to validate these findings. Standardized measurement criteria are crucial for future studies.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".