Significance of CAVI, hs-CRP and Homocysteine in subclinical arteriosclerosis among a healthy population in China
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to evaluate the ability of cardio-ankle vascular index (CAVI), high-sensitivity C-reactive protein (hs-CRP) levels and homocysteine (Hcy) levels to screen for subclinical arteriosclerosis (subAs) in an apparently healthy population, with the view to obtaining an optimal diagnostic marker or profile for subAs. METHODS: Subjects (152) undergoing routine health examinations were recruited and divided into two groups: carotid arteriosclerosis (CA) and non-carotid arteriosclerosis (NCA), according to carotid intima-media thickness (CMIT). CAVI was calculated based on blood pressure and pulse wave velocity. Serum hs-CRP and Hcy levels were also measured. A Receiver Operating Characteristic (ROC) curve was plotted to evaluate the efficacy of each in carotid arteriosclerosis screening. Ten parameter combinations, designated W1 to W10, were compared in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: The levels of all three parameters were significantly higher in the CA group, compared with the NCA group. ROC curves showed that the area under the curve (AUC) for CAVI was 0.708 (95%CI: 0.615-0.800), which is significantly larger than that of either hs-CRP (0.622) or Hcy (0.630), respectively (P < 0.001). Maximum sensitivity (100%) and NPV (100%) were attained with W10, while maximum specificity (86.2%) and PPV (46.7%) were obtained with W7. With W9, the maximum Youden index (0.416) was obtained, with a sensitivity of 77.8% and specificity of 63.8%. CONCLUSIONS: CAVI is more effective than hs-CRP or Hcy for subAs screening. The optimal profile was obtained with a combination of CAVI and other parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".