C–reactive protein levels and the association of carotid artery calcification with tooth loss
Bibliographic record
Abstract
Objectives The relationship between carotid artery calcification (CAC) and tooth loss was investigated and its association with inflammatory mediator levels was evaluated. Subjects and methods Ninety–two participants were examined for health and periodontal status. Panoramic radiographs were obtained for CAC identification. C‐reactive protein (CRP), intercellular cell adhesion molecule‐1 (ICAM–1), and vascular cell adhesion molecule‐1 (VCAM–1) levels were measured. Results Fifteen participants (16.3%) had CAC, 12 (80.0%) of whom were female. Mean age of participants with CAC was 55.3 ± 12.2 years, while that of participants without CAC was 48.9 ± 9.4 years. Median number of tooth loss in participants with CAC was 11, whereas that of individuals without CAC was 3 (P = 0.008). Age and presence of CAC were associated with the number of tooth loss, independent of health status (β = 0.452, P = <0.001 and β = 0.257, P = 0.005). Based on CRP levels, 10 participants (71.4%) were at intermediate risk of coronary heart disease (range, 1.0–2.3 μg ml−1), while four participants (28.6%) were at low risk (<1.0 μg ml−1). CRP, ICAM–1, or VCAM–1 levels were not significantly related to the presence of CAC or tooth loss. Conclusions Patients with higher tooth loss have a greater prevalence of CAC. Patients with CAC should be referred for medical consultation.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".