The Association of Intracranial Vascular Calcification and Stenosis With Acute Ischemic Cerebrovascular Events
Bibliographic record
Abstract
OBJECTIVE: The aim of this article was to evaluate the association of intracranial artery calcification (IAC) with acute downstream ischemic stroke (dAIS)/transient ischemic attack while considering stenosis. METHODS: Consecutive stroke computed tomography angiography head/neck examinations from January 2010 to April 2010 were reviewed. Per-vessel IAC and stenosis of greater than or equal to 30% were documented by 2 neuroradiologists. Associations between calcification and dAIS were assessed using multivariate logistic regression, controlling for traditional risk factors and stenosis. RESULTS: A total of 1287 arterial segments from 99 patients were reviewed. Intracranial artery calcification was significantly associated with dAIS (odds ratio [OR], 2.2; P = 0.009). This association persisted among nonstenotic arteries, with significantly higher likelihood of dAIS for arteries with IAC than those without (OR, 2.5; P = 0.009). However, among stenotic arteries, calcified stenoses had a lower association of dAIS than noncalcified stenoses (OR, 0.55; 95% confidence interval, 0.17-1.8; P = 0.33). CONCLUSIONS: Without concurrent stenosis, IAC is a significant risk factor for dAIS. When stenosis is present, IAC does not increase the association with dAIS. Stenotic and nonstenotic calcifications may represent different disease processes, as represented in the histology literature.
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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".