Age related increase in internal jugular vein size parallels temporal development of periventricular white matter hyperintensities
Bibliographic record
Abstract
Abstract Age‐related white matter hyperintensities are associated with cognitive impairment and dementia. Venous insufficiency has recently been proposed as a potential mechanism for the development of periventricular white matter hyperintensities based on the neuroanatomic distribution. The current study assesses age related changes of the internal jugular veins and its association with white matter hyperintensities. A retrospective study was performed assessing patients with computed tomography angiography (CTA) and magnetic resonance imaging (MRI) within a 4‐week window. The size of the internal jugular veins, straight sinus, vein of Galen and internal cerebral veins were measured on the CT angiography. A normalized neck venous ratio was developed. Burden of white matter hyperintensities were quantified on MRI using periventricular/deep Fazekas scores. Association was assessed using correlation analysis and multrivariate linear modeling, and differences between groups were assessed using t test, ANOVA or Kruskal–Wallis test, using p < 0.05 for significance. One hundred eighty‐two patients were included with a mean age of 65.2 ± 16.8 (51.6% females). Age was correlated with the normalized neck venous ratio (rs = 0.25, p < 0.001), and, with both, the periventricular Fazekas (rs = 0.63, p < 0.001) and the deep Fazekas (rs = 0.57, p < 0.001) grades. The periventricular Fazekas score was positively correlated with the normalized neck venous ratio (rs = 0.21, p = 0.003), but not significant on multivariate analysis accounting for age. The internal jugular veins demonstrate age related increase in size, paralleling the progression of periventricular white matter hyperintensities. Age remains the strongest predictor of white matter hyperintensities. Further work is needed to evaluate any causal role of venous changes on white matter hyperintensities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".