Aortic Arch Calcification and Vascular Disease: The Guangzhou Biobank Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To examine the association between aortic arch calcification (AAC) and vascular disease in an older Chinese sample. METHODS: For this study, 30,203 Chinese aged 50-85 years were recruited with baseline information on socioeconomic position, lifestyle and vascular risk factors. The presence and severity of AAC were diagnosed independently from chest X-ray by two radiologists. RESULTS: The age-adjusted prevalence of AAC was significantly higher in women than men [34.6% (95% CI 33.9-35.3) vs. 27.9% (95% CI 26.8-28.8), p < 0.001]. Severity of AAC was significantly associated with physician-diagnosed ischemic heart disease (adjusted OR = 1.55, 95% CI 1.35-1.79) and combined vascular disease (OR = 1.48, 95% CI 1.30-1.69) after adjusting for multiple potential confounders. Increasing severity of AAC was associated with increased risk for ischemic heart disease and vascular disease (p for trend = 0.02 to <0.001). No association between AAC and stroke was found. CONCLUSIONS: AAC was strongly and independently associated with vascular disease, suggesting that assessment of AAC from chest X-ray, which is noninvasive and relatively inexpensive, can provide useful information for risk stratification of vascular disease, and should be routinely incorporated in chest X-ray examination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".