Intima-media thickness at the near or far wall of the common carotid artery in cardiovascular risk assessment
Bibliographic record
Abstract
Abstract Aims Current guidelines recommend measuring carotid intima-media thickness (IMT) at the far wall of the common carotid artery (CCA). We aimed to precisely quantify associations of near vs. far wall CCA-IMT with the risk for atherosclerotic cardiovascular disease (CVD, defined as coronary heart disease or stroke) and their added predictive values. Methods and results We analysed individual records of 41 941 participants from 16 prospective studies in the Proof-ATHERO consortium {mean age 61 years [standard deviation (SD) = 11]; 53% female; 16% prior CVD}. Mean baseline values of near and far wall CCA-IMT were 0.83 (SD = 0.28) and 0.82 (SD = 0.27) mm, differed by a mean of 0.02 mm (95% limits of agreement: −0.40 to 0.43), and were moderately correlated [r = 0.44; 95% confidence interval (CI): 0.39–0.49). Over a median follow-up of 9.3 years, we recorded 10 423 CVD events. We pooled study-specific hazard ratios for CVD using random-effects meta-analysis. Near and far wall CCA-IMT values were approximately linearly associated with CVD risk. The respective hazard ratios per SD higher value were 1.18 (95% CI: 1.14–1.22; I² = 30.7%) and 1.20 (1.18–1.23; I² = 5.3%) when adjusted for age, sex, and prior CVD and 1.09 (1.07–1.12; I² = 8.4%) and 1.14 (1.12–1.16; I²=1.3%) upon multivariable adjustment (all P < 0.001). Assessing CCA-IMT at both walls provided a greater C-index improvement than assessing CCA-IMT at one wall only [+0.0046 vs. +0.0023 for near (P < 0.001), +0.0037 for far wall (P = 0.006)]. Conclusions The associations of near and far wall CCA-IMT with incident CVD were positive, approximately linear, and similarly strong. Improvement in risk discrimination was highest when CCA-IMT was measured at both walls.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".