Parallel Session 3: Acute/Vascular/Trauma| Wed 18 May, 1445 – 1600|4 Circulating Interleukin-6 predicts carotid study
Bibliographic record
Abstract
Background and Aims Interleukin-6 (IL-6) has important roles in atherosclerosis pathophysiology. To determine if anti-IL-6 therapy could be an adjuvant stroke prevention strategy in patients with carotid atherosclerosis, we tested whether circulating IL-6 levels predict carotid plaque severity, vulnerability, and progression in the Cardiovascular Health Study. Methods Carotid ultrasound was performed at baseline and 5 years. Plaque severity was scored 0 to 5 based on NASCET grade of stenosis. Plaque vulnerability at baseline was the presence of irregular, ulcerated or echolucent plaques. Plaque progression at 5 years was a ≥1 point increase in stenosis severity. Relationship of plasma IL-6 levels with plaque characteristics was modeled using multivariable linear (severity) or logistic (vulnerability and progression) regression. Risk factors of atherosclerosis were included as independent variables. Results In 4334 participants with complete data (58.9% women, 72.7 ± 5.1 years). There were 1267 (29.2%) participants with vulnerable plaque and 1474 (34.0%) with plaque progression. Log IL-6 predicted plaque severity (β = 0.09, p=0.04), vulnerability (OR = 1.22, 95% CI: 1.06-1.40, p=0.006) and progression (OR = 1.44, 95% CI: 1.23-1.69, p<0.001). In participants with >50% probability of progression, mean log IL-6 was 0.54 corresponding to 2.0 pg/mL. Dichotomizing IL-6 levels did not affect performance of regression models. Conclusions Plasma IL-6 predicts carotid plaque severity, vulnerability, and progression. The 2.0 pg/mL cut-off could help select individuals that would benefit from anti-IL-6 drugs for stroke prevention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.472 | 0.181 |
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".