Disease Activity Score in 28 Joints Using GGT Permits a Dual Evaluation of Joint Activity and Cardiovascular Risk
Bibliographic record
Abstract
OBJECTIVE: To identify the factors potentially associated with serum gamma-glutamyltransferase (GGT) elevation in patients with rheumatoid arthritis (RA). METHODS: This is a cross-sectional monocentric study including RA patients over a 12-month period. Data on liver function, RA disease activity, and hepatotoxic and cardiovascular (CV) risk factors were systematically collected. To provide a simple tool to evaluate both joint disease activity and CV risk factors, we constructed the Disease Activity Score in 28 joints (DAS28)-GGT composite index by replacing erythrocyte sedimentation rate (ESR) with GGT. RESULTS: Among the 129 included patients, 32 (25%) had isolated GGT increase. GGT correlated with age, C-reactive protein (CRP) levels, and body weight and were significantly increased in patients with alcohol intake, type 2 diabetes mellitus, hypertension, dyslipidemia, and metabolic syndrome. GGT levels also gradually increased with the number of CV risk factors and correlated with the Framingham CV risk score. The composite index DAS28-GGT remained a reliable marker of RA disease activity and accurately detected patients with CV risk factors. Conversely to the DAS28 and the DAS28-CRP, the DAS28-GGT steadily increased according to the number of CV risk factors and had an increased diagnostic value compared to the DAS28 and DAS28-CRP for the presence of at least 2 CV risk factors and a Framingham CV risk score greater than 10%. CONCLUSION: GGT may be considered as a marker of systemic inflammation and CV risk in patients with RA. Based on these findings, we herein propose an original index, the DAS28-GGT, which is able to evaluate both joint disease activity and CV risk. This index will deserve further validation in prospective cohorts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".