Intracranial arterial calcification as a marker of stroke risk and worse stroke outcomes in adults: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intracranial arterial calcification (ICAC) is common, but data on its impact on future stroke risk and outcomes remain limited. We conducted a systematic review and meta-analysis to investigate the association of ICAC with stroke risk and outcomes. METHODS: We searched three multidisciplinary databases from inception to July 2025. We selected studies that investigated incidence of stroke and its outcomes in patients with ICAC. We assessed the studies' risk of bias using the Newcastle Ottawa Quality Assessment Scale. Statistical analysis was conducted using Cochrane Review Manager (RevMan 5.4). RESULTS: After reviewing 660 citations, we selected 94 studies for full-text screening. We extracted data from a total of 20 studies, reporting outcomes on 14,599 patients. Overall, the risk of bias was low. The included studies were heterogeneous, with varying outcomes assessed and differing measures of associations reported. ICAC was associated with an increased risk of ischaemic stroke, with a pooled odds ratio (OR) of 2.28 (95% confidence interval (CI): 1.39-3.73), and one study reported a hazard ratio (HR) of 1.49 (95% CI: 1.24-1.78). ICAC also showed a trend toward increased mortality, with a pooled OR 1.40 (95% CI: 0.96-2.05) and high heterogenicity across the studies (I² = 65%). The pooled HR per 1-standard deviation (1-SD) increase in ICAC was 1.25 (95% CI: 1.10-1.42), with low heterogenicity (I² = 1%) between 2 studies reporting the HR. CONCLUSIONS: ICAC is significantly associated with an increased risk of stroke as well as a trend toward increased mortality (PROSPERO ID: CRD42023414813).
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".