Association between calcification of the cervical carotid artery bifurcation and white matter ischemia.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The association of cervical carotid artery bifurcation calcification to future stroke risk is unknown, though coronary artery calcification is a proven indicator of heart disease risk. Severity of white matter change has been correlated with future stroke risk. We sought to use white matter severity grade on CT as a surrogate predictor of relative future stroke risk and thus correlate white matter and future stroke risk with carotid calcification grade. METHODS: We retrospectively reviewed unenhanced neck and brain CTs in 209 patients. Carotid calcification degree was scored by the Agatston method, adapted from that commonly used to quantify coronary artery calcification. White matter change severity was scored by the European Task Force for Age-Related White Matter Change scale. Both scores were measured blinded to each other, and to age and sex covariables. Association was tested by univariate and multivariate analyses. RESULTS: Both carotid calcification and white matter scores were strongly, and independently, associated with increasing age (r = 0.61, P < .001; and r = 0.67, P < .001, respectively). Despite apparent association between carotid calcification and white matter scores on univariate analysis, there was no independent effect evident after adjusting for age as a covariant (r = 0.07, P = .14). Sex had no independent effect on white matter scores, though men had a marginally higher mean calcified carotid plaque load than women after controlling for age (P = .008). CONCLUSIONS: Carotid calcification scores do not independently predict severity of white matter ischemia. Future stroke risk, assessed by white matter severity scores, cannot be predicted from carotid calcium scores.
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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 | 0.004 |
| 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) | 0.003 | 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".